2850 lines
514 KiB
Plaintext
2850 lines
514 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "de4f7f4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.svm import LinearSVC\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "67b3d7af",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[5.1 3.5 1.4 0.2 0. ]\n",
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" [4.9 3. 1.4 0.2 0. ]\n",
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" [4.7 3.2 1.3 0.2 0. ]\n",
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" [4.6 3.1 1.5 0.2 0. ]\n",
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" [5. 3.6 1.4 0.2 0. ]]\n"
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]
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}
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],
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"source": [
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"# 1a) Ja stehen in der richtigen Dimension\n",
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"iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
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"print(iris_data[:5])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "c9c0fd1e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>.sk-global {\n",
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" /* Definition of color scheme common for light and dark mode */\n",
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" --sklearn-color-text: #000;\n",
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" --sklearn-color-text-muted: #666;\n",
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" --sklearn-color-line: gray;\n",
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" /* Definition of color scheme for unfitted estimators */\n",
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" --sklearn-color-unfitted-level-0: #fff5e6;\n",
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" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
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" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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" --sklearn-color-unfitted-level-3: chocolate;\n",
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" /* Definition of color scheme for fitted estimators */\n",
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" --sklearn-color-fitted-level-0: #f0f8ff;\n",
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" --sklearn-color-fitted-level-1: #d4ebff;\n",
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" --sklearn-color-fitted-level-2: #b3dbfd;\n",
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" --sklearn-color-fitted-level-3: cornflowerblue;\n",
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"}\n",
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"\n",
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".sk-global.light {\n",
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" /* Specific color for light theme */\n",
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" --sklearn-color-text-on-default-background: black;\n",
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" --sklearn-color-background: white;\n",
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" --sklearn-color-border-box: black;\n",
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" --sklearn-color-icon: #696969;\n",
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"}\n",
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"\n",
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".sk-global.dark {\n",
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" --sklearn-color-text-on-default-background: white;\n",
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" --sklearn-color-background: #111;\n",
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" --sklearn-color-border-box: white;\n",
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" --sklearn-color-icon: #878787;\n",
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"}\n",
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"\n",
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".sk-global {\n",
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" color: var(--sklearn-color-text);\n",
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"}\n",
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"\n",
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".sk-global pre {\n",
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" padding: 0;\n",
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"}\n",
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"\n",
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".sk-global input.sk-hidden--visually {\n",
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" border: 0;\n",
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" clip-path: inset(100%);\n",
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" height: 1px;\n",
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" margin: -1px;\n",
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" overflow: hidden;\n",
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" padding: 0;\n",
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" position: absolute;\n",
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" width: 1px;\n",
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"}\n",
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"\n",
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".sk-global div.sk-dashed-wrapped {\n",
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" border: 1px dashed var(--sklearn-color-line);\n",
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" margin: 0 0.4em 0.5em 0.4em;\n",
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" box-sizing: border-box;\n",
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" padding-bottom: 0.4em;\n",
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" background-color: var(--sklearn-color-background);\n",
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"}\n",
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"\n",
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".sk-global div.sk-container {\n",
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" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
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" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
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" so we also need the `!important` here to be able to override the\n",
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" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
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" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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" display: inline-block !important;\n",
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" position: relative;\n",
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"}\n",
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"\n",
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".sk-global div.sk-text-repr-fallback {\n",
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" display: none;\n",
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"}\n",
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"\n",
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"div.sk-parallel-item,\n",
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"div.sk-serial,\n",
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"div.sk-item {\n",
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" /* draw centered vertical line to link estimators */\n",
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" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
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" background-size: 2px 100%;\n",
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" background-repeat: no-repeat;\n",
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" background-position: center center;\n",
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"}\n",
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"\n",
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"/* Parallel-specific style estimator block */\n",
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"\n",
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".sk-global div.sk-parallel-item::after {\n",
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" content: \"\";\n",
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" width: 100%;\n",
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" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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" flex-grow: 1;\n",
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"}\n",
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"\n",
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".sk-global div.sk-parallel {\n",
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" display: flex;\n",
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" align-items: stretch;\n",
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" justify-content: center;\n",
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" background-color: var(--sklearn-color-background);\n",
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" position: relative;\n",
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"}\n",
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"\n",
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".sk-global div.sk-parallel-item {\n",
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" display: flex;\n",
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" flex-direction: column;\n",
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"}\n",
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"\n",
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".sk-global div.sk-parallel-item:first-child::after {\n",
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" align-self: flex-end;\n",
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" width: 50%;\n",
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"}\n",
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"\n",
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".sk-global div.sk-parallel-item:last-child::after {\n",
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" align-self: flex-start;\n",
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" width: 50%;\n",
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"}\n",
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"\n",
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".sk-global div.sk-parallel-item:only-child::after {\n",
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" width: 0;\n",
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"}\n",
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"\n",
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"/* Serial-specific style estimator block */\n",
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"\n",
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".sk-global div.sk-serial {\n",
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" display: flex;\n",
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" flex-direction: column;\n",
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" align-items: center;\n",
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" background-color: var(--sklearn-color-background);\n",
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" padding-right: 1em;\n",
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" padding-left: 1em;\n",
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"}\n",
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"\n",
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"\n",
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"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
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"clickable and can be expanded/collapsed.\n",
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"- Pipeline and ColumnTransformer use this feature and define the default style\n",
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"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
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"*/\n",
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"\n",
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"/* Pipeline and ColumnTransformer style (default) */\n",
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"\n",
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".sk-global div.sk-toggleable {\n",
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" /* Default theme specific background. It is overwritten whether we have a\n",
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" specific estimator or a Pipeline/ColumnTransformer */\n",
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" background-color: var(--sklearn-color-background);\n",
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"}\n",
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"\n",
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"/* Toggleable label */\n",
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".sk-global label.sk-toggleable__label {\n",
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" cursor: pointer;\n",
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" display: flex;\n",
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" width: 100%;\n",
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" margin-bottom: 0;\n",
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" padding: 0.5em;\n",
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" box-sizing: border-box;\n",
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" text-align: center;\n",
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" align-items: center;\n",
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" justify-content: center;\n",
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" gap: 0.5em;\n",
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"}\n",
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"\n",
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".sk-global label.sk-toggleable__label .caption {\n",
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" font-size: 0.6rem;\n",
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" font-weight: lighter;\n",
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" color: var(--sklearn-color-text-muted);\n",
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"}\n",
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"\n",
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".sk-global label.sk-toggleable__label-arrow:before {\n",
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" /* Arrow on the left of the label */\n",
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" content: \"▸\";\n",
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" float: left;\n",
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" margin-right: 0.25em;\n",
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" color: var(--sklearn-color-icon);\n",
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"}\n",
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"\n",
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".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
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" color: var(--sklearn-color-text);\n",
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"}\n",
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"\n",
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"/* Toggleable content - dropdown */\n",
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"\n",
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".sk-global div.sk-toggleable__content {\n",
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" display: none;\n",
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" text-align: left;\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-unfitted-level-0);\n",
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"}\n",
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"\n",
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".sk-global div.sk-toggleable__content.fitted {\n",
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" /* fitted */\n",
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" background-color: var(--sklearn-color-fitted-level-0);\n",
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"}\n",
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"\n",
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".sk-global div.sk-toggleable__content pre {\n",
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" margin: 0.2em;\n",
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" border-radius: 0.25em;\n",
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" color: var(--sklearn-color-text);\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-unfitted-level-0);\n",
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"}\n",
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"\n",
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".sk-global div.sk-toggleable__content.fitted pre {\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-fitted-level-0);\n",
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"}\n",
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"\n",
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".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
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" /* Expand drop-down */\n",
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" display: block;\n",
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" width: 100%;\n",
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" overflow: visible;\n",
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"}\n",
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"\n",
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".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
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" content: \"▾\";\n",
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"}\n",
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"\n",
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"/* Pipeline/ColumnTransformer-specific style */\n",
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"\n",
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".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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" color: var(--sklearn-color-text);\n",
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" background-color: var(--sklearn-color-unfitted-level-2);\n",
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"}\n",
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"\n",
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".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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" background-color: var(--sklearn-color-fitted-level-2);\n",
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"}\n",
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"\n",
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"/* Estimator-specific style */\n",
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"\n",
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"/* Colorize estimator box */\n",
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".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-unfitted-level-2);\n",
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"}\n",
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"\n",
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".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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" /* fitted */\n",
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" background-color: var(--sklearn-color-fitted-level-2);\n",
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"}\n",
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"\n",
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".sk-global div.sk-label label.sk-toggleable__label,\n",
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".sk-global div.sk-label label {\n",
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" /* The background is the default theme color */\n",
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" color: var(--sklearn-color-text-on-default-background);\n",
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"}\n",
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"\n",
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"/* On hover, darken the color of the background */\n",
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".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
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" color: var(--sklearn-color-text);\n",
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" background-color: var(--sklearn-color-unfitted-level-2);\n",
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"}\n",
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"\n",
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"/* Label box, darken color on hover, fitted */\n",
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".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
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" color: var(--sklearn-color-text);\n",
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" background-color: var(--sklearn-color-fitted-level-2);\n",
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"}\n",
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"\n",
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"/* Estimator label */\n",
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"\n",
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".sk-global div.sk-label label {\n",
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" font-family: monospace;\n",
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" font-weight: bold;\n",
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" line-height: 1.2em;\n",
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"}\n",
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"\n",
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".sk-global div.sk-label-container {\n",
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" text-align: center;\n",
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"}\n",
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"\n",
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"/* Estimator-specific */\n",
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".sk-global div.sk-estimator {\n",
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" font-family: monospace;\n",
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" border: 1px dotted var(--sklearn-color-border-box);\n",
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" border-radius: 0.25em;\n",
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" box-sizing: border-box;\n",
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" margin-bottom: 0.5em;\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-unfitted-level-0);\n",
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"}\n",
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"\n",
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".sk-global div.sk-estimator.fitted {\n",
|
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" /* fitted */\n",
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" background-color: var(--sklearn-color-fitted-level-0);\n",
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"}\n",
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"\n",
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"/* on hover */\n",
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".sk-global div.sk-estimator:hover {\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-unfitted-level-2);\n",
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"}\n",
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"\n",
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".sk-global div.sk-estimator.fitted:hover {\n",
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" /* fitted */\n",
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" background-color: var(--sklearn-color-fitted-level-2);\n",
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"}\n",
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"\n",
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"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
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"\n",
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"/* Common style for \"i\" and \"?\" */\n",
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"\n",
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".sk-estimator-doc-link,\n",
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"a:link.sk-estimator-doc-link,\n",
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"a:visited.sk-estimator-doc-link {\n",
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" float: right;\n",
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" font-size: smaller;\n",
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" line-height: 1em;\n",
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" font-family: monospace;\n",
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" background-color: var(--sklearn-color-unfitted-level-0);\n",
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" border-radius: 1em;\n",
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" height: 1em;\n",
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" width: 1em;\n",
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" text-decoration: none !important;\n",
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" margin-left: 0.5em;\n",
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" text-align: center;\n",
|
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" /* unfitted */\n",
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" border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
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" color: var(--sklearn-color-unfitted-level-3);\n",
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"}\n",
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"\n",
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".sk-estimator-doc-link.fitted,\n",
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"a:link.sk-estimator-doc-link.fitted,\n",
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"a:visited.sk-estimator-doc-link.fitted {\n",
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" /* fitted */\n",
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" background-color: var(--sklearn-color-fitted-level-0);\n",
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" border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
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" color: var(--sklearn-color-fitted-level-3);\n",
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"}\n",
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"\n",
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"/* On hover */\n",
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"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
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".sk-estimator-doc-link:hover,\n",
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"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
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".sk-estimator-doc-link:hover {\n",
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" /* unfitted */\n",
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" background-color: var(--sklearn-color-unfitted-level-3);\n",
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" border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
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" color: var(--sklearn-color-unfitted-level-0);\n",
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" text-decoration: none;\n",
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"}\n",
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"\n",
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"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
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".sk-estimator-doc-link.fitted:hover,\n",
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"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
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".sk-estimator-doc-link.fitted:hover {\n",
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" /* fitted */\n",
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" background-color: var(--sklearn-color-fitted-level-3);\n",
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" border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
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" color: var(--sklearn-color-fitted-level-0);\n",
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" text-decoration: none;\n",
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"}\n",
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"\n",
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"/* Span, style for the box shown on hovering the info icon */\n",
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".sk-estimator-doc-link span {\n",
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" display: none;\n",
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" z-index: 9999;\n",
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" position: relative;\n",
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" font-weight: normal;\n",
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" right: .2ex;\n",
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" padding: .5ex;\n",
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" margin: .5ex;\n",
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" width: min-content;\n",
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" min-width: 20ex;\n",
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" max-width: 50ex;\n",
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" color: var(--sklearn-color-text);\n",
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" box-shadow: 2pt 2pt 4pt #999;\n",
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" /* unfitted */\n",
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" background: var(--sklearn-color-unfitted-level-0);\n",
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" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
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"}\n",
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"\n",
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".sk-estimator-doc-link.fitted span {\n",
|
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" /* fitted */\n",
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" background: var(--sklearn-color-fitted-level-0);\n",
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" border: var(--sklearn-color-fitted-level-3);\n",
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"}\n",
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"\n",
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".sk-estimator-doc-link:hover span {\n",
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" display: block;\n",
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"}\n",
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"\n",
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"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link {\n",
|
|
" float: right;\n",
|
|
" font-size: 1rem;\n",
|
|
" line-height: 1em;\n",
|
|
" font-family: monospace;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 1rem;\n",
|
|
" height: 1rem;\n",
|
|
" width: 1rem;\n",
|
|
" text-decoration: none;\n",
|
|
" /* unfitted */\n",
|
|
" color: var(--sklearn-color-unfitted-level-1);\n",
|
|
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-1);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover */\n",
|
|
".sk-global a.estimator_doc_link:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
|
" color: var(--sklearn-color-background);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-top-container.sk-global {\n",
|
|
" /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
|
|
" We need to set it to !important and add tabindex=\"0\" in the HTML\n",
|
|
" to allow keyboard-only users to navigate the display. */\n",
|
|
" overflow-x: scroll !important;\n",
|
|
" max-width: 100%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table {\n",
|
|
" font-family: monospace;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table summary {\n",
|
|
" padding: .5rem;\n",
|
|
" cursor: pointer;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table summary::marker {\n",
|
|
" font-size: 0.7rem;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table details[open] {\n",
|
|
" padding-left: 0.1rem;\n",
|
|
" padding-right: 0.1rem;\n",
|
|
" padding-bottom: 0.3rem;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table {\n",
|
|
" margin-left: auto !important;\n",
|
|
" margin-right: auto !important;\n",
|
|
" margin-top: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:nth-child(odd) {\n",
|
|
" background-color: #fff;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:nth-child(even) {\n",
|
|
" background-color: #f6f6f6;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:hover td {\n",
|
|
" background-color: #e0e0e0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table table :is(td, th) {\n",
|
|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/*\n",
|
|
" `table td`is set in notebook with right text-align.\n",
|
|
" We need to overwrite it.\n",
|
|
"*/\n",
|
|
".estimator-table table td.param {\n",
|
|
" text-align: left;\n",
|
|
" position: relative;\n",
|
|
" padding: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td {\n",
|
|
" color:rgb(255, 94, 0);\n",
|
|
" text-align: left !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td.value {\n",
|
|
" color:rgb(255, 94, 0);\n",
|
|
" background-color: transparent;\n",
|
|
"}\n",
|
|
"\n",
|
|
".default td, .estimator-table th {\n",
|
|
" color: black;\n",
|
|
" text-align: left !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td i,\n",
|
|
".default td i {\n",
|
|
" color: black;\n",
|
|
"}\n",
|
|
"\n",
|
|
"td.fitted-att-type {\n",
|
|
" white-space: preserve nowrap;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/*\n",
|
|
" Styles for parameter documentation links\n",
|
|
" We need styling for visited so jupyter doesn't overwrite it\n",
|
|
"*/\n",
|
|
"a.param-doc-link,\n",
|
|
"a.param-doc-link:link,\n",
|
|
"a.param-doc-link:visited {\n",
|
|
" text-decoration: underline dashed;\n",
|
|
" text-underline-offset: .3em;\n",
|
|
" color: inherit;\n",
|
|
" display: block;\n",
|
|
" padding: .5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
"@supports(anchor-name: --doc-link) {\n",
|
|
" a.param-doc-link,\n",
|
|
" a.param-doc-link:link,\n",
|
|
" a.param-doc-link:visited {\n",
|
|
" anchor-name: --doc-link;\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
|
|
"a.param-doc-link::before {\n",
|
|
" position: absolute;\n",
|
|
" content: \"\";\n",
|
|
" inset: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".param-doc-description {\n",
|
|
" display: none;\n",
|
|
" position: absolute;\n",
|
|
" z-index: 9999;\n",
|
|
" left: 0;\n",
|
|
" padding: .5ex;\n",
|
|
" margin-left: 1.5em;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" box-shadow: .3em .3em .4em #999;\n",
|
|
" width: max-content;\n",
|
|
" text-align: left;\n",
|
|
" max-height: 10em;\n",
|
|
" overflow-y: auto;\n",
|
|
"\n",
|
|
" /* unfitted */\n",
|
|
" background: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: thin solid var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
"@supports(position-area: center right) {\n",
|
|
" .param-doc-description {\n",
|
|
" position-area: center right;\n",
|
|
" position: fixed;\n",
|
|
" margin-left: 0;\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Fitted state for parameter tooltips */\n",
|
|
".fitted .param-doc-description {\n",
|
|
" /* fitted */\n",
|
|
" background: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: thin solid var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".param-doc-link:hover .param-doc-description {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".copy-paste-icon {\n",
|
|
" background-image: url(data:image/svg+xml;base64,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);\n",
|
|
" background-repeat: no-repeat;\n",
|
|
" background-size: 14px 14px;\n",
|
|
" background-position: 0;\n",
|
|
" display: inline-block;\n",
|
|
" width: 14px;\n",
|
|
" height: 14px;\n",
|
|
" cursor: pointer;\n",
|
|
" border: none;\n",
|
|
" padding: 0;\n",
|
|
" background-color: transparent;\n",
|
|
"}\n",
|
|
"\n",
|
|
".copy-paste-icon:focus-visible {\n",
|
|
" outline: 1px solid currentColor;\n",
|
|
" outline-offset: 1px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features {\n",
|
|
" font-family: monospace;\n",
|
|
" cursor: pointer;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
|
" border-radius: .20em;\n",
|
|
" margin-bottom: 0.5em;\n",
|
|
" font-size: inherit; /* Needed for jupyter */\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features summary {\n",
|
|
" cursor: pointer;\n",
|
|
" display: flex;\n",
|
|
" margin-bottom: 0;\n",
|
|
" text-align: center;\n",
|
|
" align-items: center;\n",
|
|
" justify-content: center;\n",
|
|
" gap: 0.5em;\n",
|
|
" padding: .25em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details[open] > summary {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
" border-radius: .20em 0 0 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted details[open] > summary {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
" border-radius: .20em 0 0 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details > summary .arrow::before {\n",
|
|
" content: \"▸\";\n",
|
|
" color: grey;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details[open] > summary .arrow::before {\n",
|
|
" content: \"▾\";\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details:hover > summary {\n",
|
|
" margin: 0;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted details:hover > summary {\n",
|
|
" margin: 0;\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container {\n",
|
|
" max-height: 10em;\n",
|
|
" overflow: auto;\n",
|
|
" scrollbar-width: thin;\n",
|
|
" padding: .25em 0.1rem;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 0 0 .5em .5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted .features-container {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .image-container {\n",
|
|
" block-size: 1em;\n",
|
|
" inline-size: 1em;\n",
|
|
" padding: 0;\n",
|
|
" margin: 0%;\n",
|
|
" display: flex;\n",
|
|
" justify-content: center;\n",
|
|
" align-items: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .copy-paste-icon {\n",
|
|
" background-size: 1em 1em;\n",
|
|
" width: 1em;\n",
|
|
" height: 1em;\n",
|
|
" filter: grayscale(100%) opacity(60%);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table {\n",
|
|
" width: 100%;\n",
|
|
" margin: 0.01em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:nth-child(odd) {\n",
|
|
" background-color: #fff;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:nth-child(even) {\n",
|
|
" background-color: #f6f6f6;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:hover {\n",
|
|
" background-color: #e0e0e0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table {\n",
|
|
" table-layout: inherit;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table td {\n",
|
|
" text-align: left;\n",
|
|
" padding: 0 0.5em;\n",
|
|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
|
" white-space: nowrap;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
".total_features {\n",
|
|
" display: flex;\n",
|
|
" justify-content: center;\n",
|
|
" margin-top: 0.5em;\n",
|
|
"}\n",
|
|
"</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Parameters</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy penalty to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('penalty',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-penalty;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
|
|
" penalty\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-penalty;\">\n",
|
|
" penalty: {'l1', 'l2'}, default='l2'<br><br>Specifies the norm used in the penalization. The 'l2'<br>penalty is the standard used in SVC. The 'l1' leads to ``coef_``<br>vectors that are sparse.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'l2'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy loss to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('loss',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-loss;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
|
|
" loss\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-loss;\">\n",
|
|
" loss: {'hinge', 'squared_hinge'}, default='squared_hinge'<br><br>Specifies the loss function. 'hinge' is the standard SVM loss<br>(used e.g. by the SVC class) while 'squared_hinge' is the<br>square of the hinge loss. The combination of ``penalty='l1'``<br>and ``loss='hinge'`` is not supported.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'squared_hinge'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy dual to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('dual',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-dual;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
|
|
" dual\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-dual;\">\n",
|
|
" dual: "auto" or bool, default="auto"<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples > n_features.<br>`dual="auto"` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` < `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br> The `"auto"` option is added in version 1.3 and will be the default<br> in version 1.5.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'auto'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy tol to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('tol',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-tol;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
|
|
" tol\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-tol;\">\n",
|
|
" tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">0.0001</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy C to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('C',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-C;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
|
|
" C\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-C;\">\n",
|
|
" C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1.0</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy multi_class to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('multi_class',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-multi_class;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
|
|
" multi_class\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-multi_class;\">\n",
|
|
" multi_class: {'ovr', 'crammer_singer'}, default='ovr'<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``"ovr"`` trains n_classes one-vs-rest classifiers, while<br>``"crammer_singer"`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``"crammer_singer"`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'ovr'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy fit_intercept to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('fit_intercept',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-fit_intercept;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
|
|
" fit_intercept\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-fit_intercept;\">\n",
|
|
" fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy intercept_scaling to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('intercept_scaling',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
|
|
" intercept_scaling\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
|
|
" intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a "synthetic" feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy class_weight to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('class_weight',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-class_weight;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
|
|
" class_weight\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-class_weight;\">\n",
|
|
" class_weight: dict or 'balanced', default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy verbose to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('verbose',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-verbose;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
|
|
" verbose\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-verbose;\">\n",
|
|
" verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">0</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy random_state to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('random_state',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-random_state;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
|
|
" random_state\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-random_state;\">\n",
|
|
" random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy max_iter to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('max_iter',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-max_iter;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
|
|
" max_iter\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-max_iter;\">\n",
|
|
" max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1000</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" \n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Fitted attributes</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>Name</th>\n",
|
|
" <th>Type</th>\n",
|
|
" <th>Value</th>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-classes_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
|
|
" classes_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-classes_;\">\n",
|
|
" classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
|
|
" <td>[0.,1.,2.]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-coef_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
|
|
" coef_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-coef_;\">\n",
|
|
" coef_: ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
|
|
" <td>[[-1.54, 2.28],\n",
|
|
" [ 0.1 ,-0.98],\n",
|
|
" [ 0.66,-0.35]]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-intercept_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
|
|
" intercept_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-intercept_;\">\n",
|
|
" intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
|
|
" <td>[ 1.2 , 2.02,-3.14]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-n_features_in_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
|
|
" n_features_in_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-n_features_in_;\">\n",
|
|
" n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">int</td>\n",
|
|
" <td>2</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-n_iter_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
|
|
" n_iter_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-n_iter_;\">\n",
|
|
" n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">int</td>\n",
|
|
" <td>8</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" </div></div></div></div></div><script>/* Authors: The scikit-learn developers\n",
|
|
" SPDX-License-Identifier: BSD-3-Clause\n",
|
|
"*/\n",
|
|
"\n",
|
|
"function copyToClipboard(text, element) {\n",
|
|
" // Get the parameter prefix from the closest toggleable content\n",
|
|
" const toggleableContent = element.closest('.sk-toggleable__content');\n",
|
|
" const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
|
|
" const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
|
|
"\n",
|
|
" const originalStyle = element.style;\n",
|
|
" const computedStyle = window.getComputedStyle(element);\n",
|
|
" const originalWidth = computedStyle.width;\n",
|
|
" const originalHTML = element.innerHTML.replace('Copied!', '');\n",
|
|
"\n",
|
|
" navigator.clipboard.writeText(fullParamName)\n",
|
|
" .then(() => {\n",
|
|
" element.style.width = originalWidth;\n",
|
|
" element.style.color = 'green';\n",
|
|
" element.innerHTML = \"Copied!\";\n",
|
|
"\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 2000);\n",
|
|
" })\n",
|
|
" .catch(err => {\n",
|
|
" console.error('Failed to copy:', err);\n",
|
|
" element.style.color = 'red';\n",
|
|
" element.innerHTML = \"Failed!\";\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 2000);\n",
|
|
" });\n",
|
|
" return false;\n",
|
|
"}\n",
|
|
"\n",
|
|
"document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
|
|
" const toggleableContent = element.closest('.sk-toggleable__content');\n",
|
|
" const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
|
|
"\n",
|
|
" const parent = element.parentElement;\n",
|
|
" if (!parent || !parent.nextElementSibling) {\n",
|
|
" console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
|
|
" return;\n",
|
|
" }\n",
|
|
"\n",
|
|
" const paramName = element.parentElement.nextElementSibling\n",
|
|
" .textContent.trim().split(' ')[0];\n",
|
|
" const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
|
|
"\n",
|
|
" element.setAttribute('title', fullParamName);\n",
|
|
"});\n",
|
|
"\n",
|
|
"/**\n",
|
|
" * Copy the list of feature names formatted as a Python list.\n",
|
|
" *\n",
|
|
" * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
|
|
" * contain a `details` element and a table containing feature named.\n",
|
|
" * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
|
|
" */\n",
|
|
"function copyFeatureNamesToClipboard(element) {\n",
|
|
" var detailsElem = element.closest('.features').querySelector('details');\n",
|
|
" var wasOpen = detailsElem.open;\n",
|
|
" detailsElem.open = true;\n",
|
|
" var content = element.closest('.features').querySelector('tbody')\n",
|
|
" .innerText.trim();\n",
|
|
" if (!wasOpen) detailsElem.open = false;\n",
|
|
" const rows = content.split('\\n').map(row => ` \"${row}\"`);\n",
|
|
" const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
|
|
" const originalHTML = element.innerHTML.replace('✔', '');\n",
|
|
" const originalStyle = element.style;\n",
|
|
" const copyMark = document.createElement('span');\n",
|
|
" copyMark.innerHTML = '✔';\n",
|
|
" copyMark.style.color = 'blue';\n",
|
|
" copyMark.style.fontSize = '1em';\n",
|
|
"\n",
|
|
" navigator.clipboard.writeText(formattedText)\n",
|
|
" .then(() => {\n",
|
|
" element.style.display = 'none';\n",
|
|
" element.parentElement.appendChild(copyMark);\n",
|
|
"\n",
|
|
" setTimeout(() => {\n",
|
|
" copyMark.remove();\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 1000);\n",
|
|
" })\n",
|
|
" .catch(err => {\n",
|
|
" console.error('Failed to copy:', err);\n",
|
|
" element.style.color = 'orange';\n",
|
|
" element.innerHTML = \"Failed!\";\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 1000);\n",
|
|
" });\n",
|
|
" return false;\n",
|
|
"}\n",
|
|
"/**\n",
|
|
" * Adapted from Skrub\n",
|
|
" * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
|
|
" * @returns \"light\" or \"dark\"\n",
|
|
" */\n",
|
|
"function detectTheme(element) {\n",
|
|
" const body = document.querySelector('body');\n",
|
|
"\n",
|
|
" // Check VSCode theme\n",
|
|
" const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
|
|
" const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
|
|
"\n",
|
|
" if (themeKindAttr && themeNameAttr) {\n",
|
|
" const themeKind = themeKindAttr.toLowerCase();\n",
|
|
" const themeName = themeNameAttr.toLowerCase();\n",
|
|
"\n",
|
|
" if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
|
|
" return \"dark\";\n",
|
|
" }\n",
|
|
" if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
|
|
" return \"light\";\n",
|
|
" }\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Check Jupyter theme\n",
|
|
" if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
|
|
" return 'dark';\n",
|
|
" } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
|
|
" return 'light';\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Guess based on a parent element's color\n",
|
|
" const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
|
|
" const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
|
|
" if (match) {\n",
|
|
" const [r, g, b] = [\n",
|
|
" parseFloat(match[1]),\n",
|
|
" parseFloat(match[2]),\n",
|
|
" parseFloat(match[3])\n",
|
|
" ];\n",
|
|
"\n",
|
|
" // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
|
|
" const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
|
|
"\n",
|
|
" if (luma > 180) {\n",
|
|
" // If the text is very bright we have a dark theme\n",
|
|
" return 'dark';\n",
|
|
" }\n",
|
|
" if (luma < 75) {\n",
|
|
" // If the text is very dark we have a light theme\n",
|
|
" return 'light';\n",
|
|
" }\n",
|
|
" // Otherwise fall back to the next heuristic.\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Fallback to system preference\n",
|
|
" return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"function forceTheme(elementId) {\n",
|
|
" const estimatorElement = document.querySelector(`#${elementId}`);\n",
|
|
" if (estimatorElement === null) {\n",
|
|
" console.error(`Element with id ${elementId} not found.`);\n",
|
|
" } else {\n",
|
|
" const theme = detectTheme(estimatorElement);\n",
|
|
" estimatorElement.classList.add(theme);\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"forceTheme('sk-container-id-1');</script></body>"
|
|
],
|
|
"text/plain": [
|
|
"LinearSVC()"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 1b)\n",
|
|
"clf = LinearSVC()\n",
|
|
"X = iris_data[:,[0,1]]\n",
|
|
"T = iris_data[:,4]\n",
|
|
"clf.fit(X, T)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "cc181a3a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"80.0 Prozent wurden richtig vorausgesagt\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 1 c+d)\n",
|
|
"pred = clf.predict(X)\n",
|
|
"correct_perc = (sum(pred == T)/len(pred))*100\n",
|
|
"\n",
|
|
"print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "16af32f1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 1e)\n",
|
|
"import nnwplot\n",
|
|
"nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "f4bcee12",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"<Figure size 640x480 with 1 Axes>"
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]
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 1f)\n",
|
|
"from sklearn.svm import SVC\n",
|
|
"svc = SVC()\n",
|
|
"svc.fit(X, T)\n",
|
|
"nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "2c8991de",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 1g)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "1a5e84f6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#1h)\n",
|
|
"X_0_3 = iris_data[:,[0,3]]\n",
|
|
"clf.fit(X_0_3, T)\n",
|
|
"nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "892baf25",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"X_0_3 = iris_data[:,[0,3]]\n",
|
|
"svc.fit(X_0_3, T)\n",
|
|
"nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "1bad3e36",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 1i)\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"knc = KNeighborsClassifier()\n",
|
|
"knc.fit(X,T)\n",
|
|
"nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
|
|
"knc.fit(X_0_3,T)\n",
|
|
"nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "d79741d2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 3, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3,\n",
|
|
" 0, 3, 0, 3, 0, 3, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
|
|
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
|
|
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1,\n",
|
|
" 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2], dtype=int32)"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2a) probabilistisches Verfahren\n",
|
|
"from sklearn.cluster import KMeans\n",
|
|
"\n",
|
|
"X_0_2 = iris_data[:,[0,2]]\n",
|
|
"km = KMeans(4)\n",
|
|
"km.fit(X_0_2)\n",
|
|
"km.predict(X_0_2)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "88b3d717",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 3a)\n",
|
|
"X = np.linspace(0, 2)\n",
|
|
"np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
|
|
"X = np.sort(np.random.rand(50)*2)\n",
|
|
"T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
|
|
"plt.plot(X, T, '.')\n",
|
|
"plt.xlabel('x')\n",
|
|
"plt.ylabel('t')\n",
|
|
"plt.show()\n",
|
|
"# Es gibt 50 Merkmale in den Daten"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "b251a2c1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
|
|
" 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
|
|
" 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
|
|
" 0.5842893 0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
|
|
" 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
|
|
" 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
|
|
" 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
|
|
" 1.6167947 1.66488528 1.73235229 1.8186408 1.89777107 1.90142861\n",
|
|
" 1.93126407 1.9398197 ]\n",
|
|
"[[0.04116899]\n",
|
|
" [0.06877704]\n",
|
|
" [0.09290083]\n",
|
|
" [0.11616722]\n",
|
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" [0.13010319]\n",
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" [1.93126407]\n",
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{
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"data": {
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"text/html": [
|
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"<style>.sk-global {\n",
|
|
" /* Definition of color scheme common for light and dark mode */\n",
|
|
" --sklearn-color-text: #000;\n",
|
|
" --sklearn-color-text-muted: #666;\n",
|
|
" --sklearn-color-line: gray;\n",
|
|
" /* Definition of color scheme for unfitted estimators */\n",
|
|
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
|
|
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
|
|
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
|
|
" --sklearn-color-unfitted-level-3: chocolate;\n",
|
|
" /* Definition of color scheme for fitted estimators */\n",
|
|
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
|
|
" --sklearn-color-fitted-level-1: #d4ebff;\n",
|
|
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
|
|
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global.light {\n",
|
|
" /* Specific color for light theme */\n",
|
|
" --sklearn-color-text-on-default-background: black;\n",
|
|
" --sklearn-color-background: white;\n",
|
|
" --sklearn-color-border-box: black;\n",
|
|
" --sklearn-color-icon: #696969;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global.dark {\n",
|
|
" --sklearn-color-text-on-default-background: white;\n",
|
|
" --sklearn-color-background: #111;\n",
|
|
" --sklearn-color-border-box: white;\n",
|
|
" --sklearn-color-icon: #878787;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global pre {\n",
|
|
" padding: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global input.sk-hidden--visually {\n",
|
|
" border: 0;\n",
|
|
" clip-path: inset(100%);\n",
|
|
" height: 1px;\n",
|
|
" margin: -1px;\n",
|
|
" overflow: hidden;\n",
|
|
" padding: 0;\n",
|
|
" position: absolute;\n",
|
|
" width: 1px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-dashed-wrapped {\n",
|
|
" border: 1px dashed var(--sklearn-color-line);\n",
|
|
" margin: 0 0.4em 0.5em 0.4em;\n",
|
|
" box-sizing: border-box;\n",
|
|
" padding-bottom: 0.4em;\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-container {\n",
|
|
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
|
|
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
|
|
" so we also need the `!important` here to be able to override the\n",
|
|
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
|
|
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
|
|
" display: inline-block !important;\n",
|
|
" position: relative;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-text-repr-fallback {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
"div.sk-parallel-item,\n",
|
|
"div.sk-serial,\n",
|
|
"div.sk-item {\n",
|
|
" /* draw centered vertical line to link estimators */\n",
|
|
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
|
|
" background-size: 2px 100%;\n",
|
|
" background-repeat: no-repeat;\n",
|
|
" background-position: center center;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Parallel-specific style estimator block */\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item::after {\n",
|
|
" content: \"\";\n",
|
|
" width: 100%;\n",
|
|
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
|
|
" flex-grow: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel {\n",
|
|
" display: flex;\n",
|
|
" align-items: stretch;\n",
|
|
" justify-content: center;\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
" position: relative;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item {\n",
|
|
" display: flex;\n",
|
|
" flex-direction: column;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item:first-child::after {\n",
|
|
" align-self: flex-end;\n",
|
|
" width: 50%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item:last-child::after {\n",
|
|
" align-self: flex-start;\n",
|
|
" width: 50%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item:only-child::after {\n",
|
|
" width: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Serial-specific style estimator block */\n",
|
|
"\n",
|
|
".sk-global div.sk-serial {\n",
|
|
" display: flex;\n",
|
|
" flex-direction: column;\n",
|
|
" align-items: center;\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
" padding-right: 1em;\n",
|
|
" padding-left: 1em;\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
|
|
"clickable and can be expanded/collapsed.\n",
|
|
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
|
|
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
|
|
"*/\n",
|
|
"\n",
|
|
"/* Pipeline and ColumnTransformer style (default) */\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable {\n",
|
|
" /* Default theme specific background. It is overwritten whether we have a\n",
|
|
" specific estimator or a Pipeline/ColumnTransformer */\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Toggleable label */\n",
|
|
".sk-global label.sk-toggleable__label {\n",
|
|
" cursor: pointer;\n",
|
|
" display: flex;\n",
|
|
" width: 100%;\n",
|
|
" margin-bottom: 0;\n",
|
|
" padding: 0.5em;\n",
|
|
" box-sizing: border-box;\n",
|
|
" text-align: center;\n",
|
|
" align-items: center;\n",
|
|
" justify-content: center;\n",
|
|
" gap: 0.5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global label.sk-toggleable__label .caption {\n",
|
|
" font-size: 0.6rem;\n",
|
|
" font-weight: lighter;\n",
|
|
" color: var(--sklearn-color-text-muted);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global label.sk-toggleable__label-arrow:before {\n",
|
|
" /* Arrow on the left of the label */\n",
|
|
" content: \"▸\";\n",
|
|
" float: left;\n",
|
|
" margin-right: 0.25em;\n",
|
|
" color: var(--sklearn-color-icon);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Toggleable content - dropdown */\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content {\n",
|
|
" display: none;\n",
|
|
" text-align: left;\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content pre {\n",
|
|
" margin: 0.2em;\n",
|
|
" border-radius: 0.25em;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content.fitted pre {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
|
|
" /* Expand drop-down */\n",
|
|
" display: block;\n",
|
|
" width: 100%;\n",
|
|
" overflow: visible;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
|
|
" content: \"▾\";\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Pipeline/ColumnTransformer-specific style */\n",
|
|
"\n",
|
|
".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator-specific style */\n",
|
|
"\n",
|
|
"/* Colorize estimator box */\n",
|
|
".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-label label.sk-toggleable__label,\n",
|
|
".sk-global div.sk-label label {\n",
|
|
" /* The background is the default theme color */\n",
|
|
" color: var(--sklearn-color-text-on-default-background);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover, darken the color of the background */\n",
|
|
".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Label box, darken color on hover, fitted */\n",
|
|
".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator label */\n",
|
|
"\n",
|
|
".sk-global div.sk-label label {\n",
|
|
" font-family: monospace;\n",
|
|
" font-weight: bold;\n",
|
|
" line-height: 1.2em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-label-container {\n",
|
|
" text-align: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator-specific */\n",
|
|
".sk-global div.sk-estimator {\n",
|
|
" font-family: monospace;\n",
|
|
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
|
" border-radius: 0.25em;\n",
|
|
" box-sizing: border-box;\n",
|
|
" margin-bottom: 0.5em;\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-estimator.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* on hover */\n",
|
|
".sk-global div.sk-estimator:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-estimator.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
|
|
"\n",
|
|
"/* Common style for \"i\" and \"?\" */\n",
|
|
"\n",
|
|
".sk-estimator-doc-link,\n",
|
|
"a:link.sk-estimator-doc-link,\n",
|
|
"a:visited.sk-estimator-doc-link {\n",
|
|
" float: right;\n",
|
|
" font-size: smaller;\n",
|
|
" line-height: 1em;\n",
|
|
" font-family: monospace;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 1em;\n",
|
|
" height: 1em;\n",
|
|
" width: 1em;\n",
|
|
" text-decoration: none !important;\n",
|
|
" margin-left: 0.5em;\n",
|
|
" text-align: center;\n",
|
|
" /* unfitted */\n",
|
|
" border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
|
|
" color: var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-estimator-doc-link.fitted,\n",
|
|
"a:link.sk-estimator-doc-link.fitted,\n",
|
|
"a:visited.sk-estimator-doc-link.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover */\n",
|
|
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
|
|
".sk-estimator-doc-link:hover,\n",
|
|
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
|
|
".sk-estimator-doc-link:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
|
" border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
|
|
" color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
|
|
".sk-estimator-doc-link.fitted:hover,\n",
|
|
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
|
|
".sk-estimator-doc-link.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
|
" border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-0);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Span, style for the box shown on hovering the info icon */\n",
|
|
".sk-estimator-doc-link span {\n",
|
|
" display: none;\n",
|
|
" z-index: 9999;\n",
|
|
" position: relative;\n",
|
|
" font-weight: normal;\n",
|
|
" right: .2ex;\n",
|
|
" padding: .5ex;\n",
|
|
" margin: .5ex;\n",
|
|
" width: min-content;\n",
|
|
" min-width: 20ex;\n",
|
|
" max-width: 50ex;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" box-shadow: 2pt 2pt 4pt #999;\n",
|
|
" /* unfitted */\n",
|
|
" background: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-estimator-doc-link.fitted span {\n",
|
|
" /* fitted */\n",
|
|
" background: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-estimator-doc-link:hover span {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link {\n",
|
|
" float: right;\n",
|
|
" font-size: 1rem;\n",
|
|
" line-height: 1em;\n",
|
|
" font-family: monospace;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 1rem;\n",
|
|
" height: 1rem;\n",
|
|
" width: 1rem;\n",
|
|
" text-decoration: none;\n",
|
|
" /* unfitted */\n",
|
|
" color: var(--sklearn-color-unfitted-level-1);\n",
|
|
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-1);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover */\n",
|
|
".sk-global a.estimator_doc_link:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
|
" color: var(--sklearn-color-background);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-top-container.sk-global {\n",
|
|
" /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
|
|
" We need to set it to !important and add tabindex=\"0\" in the HTML\n",
|
|
" to allow keyboard-only users to navigate the display. */\n",
|
|
" overflow-x: scroll !important;\n",
|
|
" max-width: 100%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table {\n",
|
|
" font-family: monospace;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table summary {\n",
|
|
" padding: .5rem;\n",
|
|
" cursor: pointer;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table summary::marker {\n",
|
|
" font-size: 0.7rem;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table details[open] {\n",
|
|
" padding-left: 0.1rem;\n",
|
|
" padding-right: 0.1rem;\n",
|
|
" padding-bottom: 0.3rem;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table {\n",
|
|
" margin-left: auto !important;\n",
|
|
" margin-right: auto !important;\n",
|
|
" margin-top: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:nth-child(odd) {\n",
|
|
" background-color: #fff;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:nth-child(even) {\n",
|
|
" background-color: #f6f6f6;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:hover td {\n",
|
|
" background-color: #e0e0e0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table table :is(td, th) {\n",
|
|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/*\n",
|
|
" `table td`is set in notebook with right text-align.\n",
|
|
" We need to overwrite it.\n",
|
|
"*/\n",
|
|
".estimator-table table td.param {\n",
|
|
" text-align: left;\n",
|
|
" position: relative;\n",
|
|
" padding: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td {\n",
|
|
" color:rgb(255, 94, 0);\n",
|
|
" text-align: left !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td.value {\n",
|
|
" color:rgb(255, 94, 0);\n",
|
|
" background-color: transparent;\n",
|
|
"}\n",
|
|
"\n",
|
|
".default td, .estimator-table th {\n",
|
|
" color: black;\n",
|
|
" text-align: left !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td i,\n",
|
|
".default td i {\n",
|
|
" color: black;\n",
|
|
"}\n",
|
|
"\n",
|
|
"td.fitted-att-type {\n",
|
|
" white-space: preserve nowrap;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/*\n",
|
|
" Styles for parameter documentation links\n",
|
|
" We need styling for visited so jupyter doesn't overwrite it\n",
|
|
"*/\n",
|
|
"a.param-doc-link,\n",
|
|
"a.param-doc-link:link,\n",
|
|
"a.param-doc-link:visited {\n",
|
|
" text-decoration: underline dashed;\n",
|
|
" text-underline-offset: .3em;\n",
|
|
" color: inherit;\n",
|
|
" display: block;\n",
|
|
" padding: .5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
"@supports(anchor-name: --doc-link) {\n",
|
|
" a.param-doc-link,\n",
|
|
" a.param-doc-link:link,\n",
|
|
" a.param-doc-link:visited {\n",
|
|
" anchor-name: --doc-link;\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
|
|
"a.param-doc-link::before {\n",
|
|
" position: absolute;\n",
|
|
" content: \"\";\n",
|
|
" inset: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".param-doc-description {\n",
|
|
" display: none;\n",
|
|
" position: absolute;\n",
|
|
" z-index: 9999;\n",
|
|
" left: 0;\n",
|
|
" padding: .5ex;\n",
|
|
" margin-left: 1.5em;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" box-shadow: .3em .3em .4em #999;\n",
|
|
" width: max-content;\n",
|
|
" text-align: left;\n",
|
|
" max-height: 10em;\n",
|
|
" overflow-y: auto;\n",
|
|
"\n",
|
|
" /* unfitted */\n",
|
|
" background: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: thin solid var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
"@supports(position-area: center right) {\n",
|
|
" .param-doc-description {\n",
|
|
" position-area: center right;\n",
|
|
" position: fixed;\n",
|
|
" margin-left: 0;\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Fitted state for parameter tooltips */\n",
|
|
".fitted .param-doc-description {\n",
|
|
" /* fitted */\n",
|
|
" background: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: thin solid var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".param-doc-link:hover .param-doc-description {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".copy-paste-icon {\n",
|
|
" background-image: url(data:image/svg+xml;base64,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);\n",
|
|
" background-repeat: no-repeat;\n",
|
|
" background-size: 14px 14px;\n",
|
|
" background-position: 0;\n",
|
|
" display: inline-block;\n",
|
|
" width: 14px;\n",
|
|
" height: 14px;\n",
|
|
" cursor: pointer;\n",
|
|
" border: none;\n",
|
|
" padding: 0;\n",
|
|
" background-color: transparent;\n",
|
|
"}\n",
|
|
"\n",
|
|
".copy-paste-icon:focus-visible {\n",
|
|
" outline: 1px solid currentColor;\n",
|
|
" outline-offset: 1px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features {\n",
|
|
" font-family: monospace;\n",
|
|
" cursor: pointer;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
|
" border-radius: .20em;\n",
|
|
" margin-bottom: 0.5em;\n",
|
|
" font-size: inherit; /* Needed for jupyter */\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features summary {\n",
|
|
" cursor: pointer;\n",
|
|
" display: flex;\n",
|
|
" margin-bottom: 0;\n",
|
|
" text-align: center;\n",
|
|
" align-items: center;\n",
|
|
" justify-content: center;\n",
|
|
" gap: 0.5em;\n",
|
|
" padding: .25em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details[open] > summary {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
" border-radius: .20em 0 0 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted details[open] > summary {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
" border-radius: .20em 0 0 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details > summary .arrow::before {\n",
|
|
" content: \"▸\";\n",
|
|
" color: grey;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details[open] > summary .arrow::before {\n",
|
|
" content: \"▾\";\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details:hover > summary {\n",
|
|
" margin: 0;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted details:hover > summary {\n",
|
|
" margin: 0;\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container {\n",
|
|
" max-height: 10em;\n",
|
|
" overflow: auto;\n",
|
|
" scrollbar-width: thin;\n",
|
|
" padding: .25em 0.1rem;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 0 0 .5em .5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted .features-container {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .image-container {\n",
|
|
" block-size: 1em;\n",
|
|
" inline-size: 1em;\n",
|
|
" padding: 0;\n",
|
|
" margin: 0%;\n",
|
|
" display: flex;\n",
|
|
" justify-content: center;\n",
|
|
" align-items: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .copy-paste-icon {\n",
|
|
" background-size: 1em 1em;\n",
|
|
" width: 1em;\n",
|
|
" height: 1em;\n",
|
|
" filter: grayscale(100%) opacity(60%);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table {\n",
|
|
" width: 100%;\n",
|
|
" margin: 0.01em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:nth-child(odd) {\n",
|
|
" background-color: #fff;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:nth-child(even) {\n",
|
|
" background-color: #f6f6f6;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:hover {\n",
|
|
" background-color: #e0e0e0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table {\n",
|
|
" table-layout: inherit;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table td {\n",
|
|
" text-align: left;\n",
|
|
" padding: 0 0.5em;\n",
|
|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
|
" white-space: nowrap;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
".total_features {\n",
|
|
" display: flex;\n",
|
|
" justify-content: center;\n",
|
|
" margin-top: 0.5em;\n",
|
|
"}\n",
|
|
"</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Parameters</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy fit_intercept to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('fit_intercept',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-fit_intercept;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
|
|
" fit_intercept\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-fit_intercept;\">\n",
|
|
" fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy copy_X to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('copy_X',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-copy_X;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
|
|
" copy_X\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-copy_X;\">\n",
|
|
" copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy tol to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('tol',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-tol;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
|
|
" tol\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-tol;\">\n",
|
|
" tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br> Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1e-06</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy n_jobs to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('n_jobs',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-n_jobs;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
|
|
" n_jobs\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-n_jobs;\">\n",
|
|
" n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets > 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy positive to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('positive',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-positive;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
|
|
" positive\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-positive;\">\n",
|
|
" positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">False</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" \n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Fitted attributes</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>Name</th>\n",
|
|
" <th>Type</th>\n",
|
|
" <th>Value</th>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-coef_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
|
|
" coef_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-coef_;\">\n",
|
|
" coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
|
|
" <td>[1.64]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-intercept_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
|
|
" intercept_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-intercept_;\">\n",
|
|
" intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">float64</td>\n",
|
|
" <td>-0.1418</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-n_features_in_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
|
|
" n_features_in_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-n_features_in_;\">\n",
|
|
" n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">int</td>\n",
|
|
" <td>1</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-rank_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
|
|
" rank_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-rank_;\">\n",
|
|
" rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">int64</td>\n",
|
|
" <td>np.int64(1)</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-singular_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
|
|
" singular_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-singular_;\">\n",
|
|
" singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
|
|
" <td>[4.04]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" </div></div></div></div></div><script>/* Authors: The scikit-learn developers\n",
|
|
" SPDX-License-Identifier: BSD-3-Clause\n",
|
|
"*/\n",
|
|
"\n",
|
|
"function copyToClipboard(text, element) {\n",
|
|
" // Get the parameter prefix from the closest toggleable content\n",
|
|
" const toggleableContent = element.closest('.sk-toggleable__content');\n",
|
|
" const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
|
|
" const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
|
|
"\n",
|
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" const originalStyle = element.style;\n",
|
|
" const computedStyle = window.getComputedStyle(element);\n",
|
|
" const originalWidth = computedStyle.width;\n",
|
|
" const originalHTML = element.innerHTML.replace('Copied!', '');\n",
|
|
"\n",
|
|
" navigator.clipboard.writeText(fullParamName)\n",
|
|
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|
|
" element.style.width = originalWidth;\n",
|
|
" element.style.color = 'green';\n",
|
|
" element.innerHTML = \"Copied!\";\n",
|
|
"\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 2000);\n",
|
|
" })\n",
|
|
" .catch(err => {\n",
|
|
" console.error('Failed to copy:', err);\n",
|
|
" element.style.color = 'red';\n",
|
|
" element.innerHTML = \"Failed!\";\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 2000);\n",
|
|
" });\n",
|
|
" return false;\n",
|
|
"}\n",
|
|
"\n",
|
|
"document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
|
|
" const toggleableContent = element.closest('.sk-toggleable__content');\n",
|
|
" const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
|
|
"\n",
|
|
" const parent = element.parentElement;\n",
|
|
" if (!parent || !parent.nextElementSibling) {\n",
|
|
" console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
|
|
" return;\n",
|
|
" }\n",
|
|
"\n",
|
|
" const paramName = element.parentElement.nextElementSibling\n",
|
|
" .textContent.trim().split(' ')[0];\n",
|
|
" const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
|
|
"\n",
|
|
" element.setAttribute('title', fullParamName);\n",
|
|
"});\n",
|
|
"\n",
|
|
"/**\n",
|
|
" * Copy the list of feature names formatted as a Python list.\n",
|
|
" *\n",
|
|
" * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
|
|
" * contain a `details` element and a table containing feature named.\n",
|
|
" * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
|
|
" */\n",
|
|
"function copyFeatureNamesToClipboard(element) {\n",
|
|
" var detailsElem = element.closest('.features').querySelector('details');\n",
|
|
" var wasOpen = detailsElem.open;\n",
|
|
" detailsElem.open = true;\n",
|
|
" var content = element.closest('.features').querySelector('tbody')\n",
|
|
" .innerText.trim();\n",
|
|
" if (!wasOpen) detailsElem.open = false;\n",
|
|
" const rows = content.split('\\n').map(row => ` \"${row}\"`);\n",
|
|
" const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
|
|
" const originalHTML = element.innerHTML.replace('✔', '');\n",
|
|
" const originalStyle = element.style;\n",
|
|
" const copyMark = document.createElement('span');\n",
|
|
" copyMark.innerHTML = '✔';\n",
|
|
" copyMark.style.color = 'blue';\n",
|
|
" copyMark.style.fontSize = '1em';\n",
|
|
"\n",
|
|
" navigator.clipboard.writeText(formattedText)\n",
|
|
" .then(() => {\n",
|
|
" element.style.display = 'none';\n",
|
|
" element.parentElement.appendChild(copyMark);\n",
|
|
"\n",
|
|
" setTimeout(() => {\n",
|
|
" copyMark.remove();\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 1000);\n",
|
|
" })\n",
|
|
" .catch(err => {\n",
|
|
" console.error('Failed to copy:', err);\n",
|
|
" element.style.color = 'orange';\n",
|
|
" element.innerHTML = \"Failed!\";\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 1000);\n",
|
|
" });\n",
|
|
" return false;\n",
|
|
"}\n",
|
|
"/**\n",
|
|
" * Adapted from Skrub\n",
|
|
" * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
|
|
" * @returns \"light\" or \"dark\"\n",
|
|
" */\n",
|
|
"function detectTheme(element) {\n",
|
|
" const body = document.querySelector('body');\n",
|
|
"\n",
|
|
" // Check VSCode theme\n",
|
|
" const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
|
|
" const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
|
|
"\n",
|
|
" if (themeKindAttr && themeNameAttr) {\n",
|
|
" const themeKind = themeKindAttr.toLowerCase();\n",
|
|
" const themeName = themeNameAttr.toLowerCase();\n",
|
|
"\n",
|
|
" if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
|
|
" return \"dark\";\n",
|
|
" }\n",
|
|
" if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
|
|
" return \"light\";\n",
|
|
" }\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Check Jupyter theme\n",
|
|
" if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
|
|
" return 'dark';\n",
|
|
" } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
|
|
" return 'light';\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Guess based on a parent element's color\n",
|
|
" const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
|
|
" const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
|
|
" if (match) {\n",
|
|
" const [r, g, b] = [\n",
|
|
" parseFloat(match[1]),\n",
|
|
" parseFloat(match[2]),\n",
|
|
" parseFloat(match[3])\n",
|
|
" ];\n",
|
|
"\n",
|
|
" // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
|
|
" const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
|
|
"\n",
|
|
" if (luma > 180) {\n",
|
|
" // If the text is very bright we have a dark theme\n",
|
|
" return 'dark';\n",
|
|
" }\n",
|
|
" if (luma < 75) {\n",
|
|
" // If the text is very dark we have a light theme\n",
|
|
" return 'light';\n",
|
|
" }\n",
|
|
" // Otherwise fall back to the next heuristic.\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Fallback to system preference\n",
|
|
" return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"function forceTheme(elementId) {\n",
|
|
" const estimatorElement = document.querySelector(`#${elementId}`);\n",
|
|
" if (estimatorElement === null) {\n",
|
|
" console.error(`Element with id ${elementId} not found.`);\n",
|
|
" } else {\n",
|
|
" const theme = detectTheme(estimatorElement);\n",
|
|
" estimatorElement.classList.add(theme);\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"forceTheme('sk-container-id-2');</script></body>"
|
|
],
|
|
"text/plain": [
|
|
"LinearRegression()"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 3b)\n",
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|
"reg = LinearRegression()\n",
|
|
"print(X)\n",
|
|
"print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
|
|
"reg.fit(X.reshape(-1,1),T)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "98476c93",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[-0.07437258 -0.0291456 0.0103735 0.04848806 0.07131768 0.17819428\n",
|
|
" 0.25802648 0.31521741 0.36927952 0.36935855 0.41688375 0.45390939\n",
|
|
" 0.45908454 0.46383508 0.51238855 0.55388473 0.70604178 0.81235693\n",
|
|
" 0.81535647 0.85499255 0.85620981 0.87946314 1.05851877 1.08531376\n",
|
|
" 1.27339292 1.30028357 1.35243501 1.48056341 1.54300275 1.56211567\n",
|
|
" 1.57747659 1.64940532 1.79914906 1.81960638 1.82765485 1.84872133\n",
|
|
" 1.86283603 2.02884723 2.09997946 2.17808643 2.25646145 2.43070493\n",
|
|
" 2.50678657 2.58556763 2.69609076 2.83744691 2.96707657 2.97306828\n",
|
|
" 3.02194413 3.03595981]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 3c)\n",
|
|
"plt.plot(X, T, '.')\n",
|
|
"plt.xlabel('x')\n",
|
|
"plt.ylabel('t')\n",
|
|
"plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
|
|
"plt.show()\n",
|
|
"print(reg.predict(X.reshape(-1,1)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "94ab4bd3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 3d) Decision Tree Regressor\n",
|
|
"from sklearn.tree import DecisionTreeRegressor\n",
|
|
"from sklearn.svm import SVR\n",
|
|
"\n",
|
|
"dtr = DecisionTreeRegressor()\n",
|
|
"dtr.fit(X.reshape(-1,1),T)\n",
|
|
"\n",
|
|
"plt.plot(X, T, '.')\n",
|
|
"plt.xlabel('x')\n",
|
|
"plt.ylabel('t')\n",
|
|
"plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "475aa6e4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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HD+NwOLj11ltLsXIRESl1v30Ax/6C8ErQ/RmrqxEfZlnwqVu3LsOHD8fhMCeTK1u2LN26dWPPnj2F7jNjxgyOHz/Os88+C4DD4WDMmDEsXLiQnTt3lkrdIiJSypJ2w+I3zOVer0DZKEvLEd9m+YhP27ZtIzk5mW3btvHee++5Qk1Bfv/9d+rUqUNMTIxrXceOHd0eExER35WYksGuo+nUjg0nLirUXDnnacjJhFqXQfNB1hYoPs/y4PPee++xZMkSdu3aRadOnRgwYECh2x47dowKFdwvXSxfvjx2u51jx44VuE9WVhZZWVmu+6mpqZ4pXEREPGrKqr2MnrYRpwF2G4wd0JzBEZtg2xywB0Oft8Bms7pM8XGWd4n/97//zfr16zl48CDR0dFceeWVZGdnF7htSEiIW4gB86owp9NJSEhIgfuMHTuWqKgo1y0+Pt7jxyAiIhcnMSXDFXoAnAa8OG0NOT88Ya7oPBIqNrSuQPEblgefPGFhYTz22GNs376dP//8s8BtatSowYEDB9zW5V3RVaNGjQL3GT16NCkpKa5bQkKCZwsXEZGLtutouiv05LkvaDrBqfsgKh66PmFNYeJ3LAs+mZmZ+dbt3r0bME9fgTlez+rVq12nsa688koOHjzI77//7tpn5syZhIWF0alTpwJfx+FwEBkZ6XYTERHvUjs2HPsZZ7Hq2vYzPGiWeefqV6FMuDWFid+xLPh8+OGH3HXXXUybNo3Fixfz/vvv8+CDD3LHHXdQrVo1AE6cOEH79u2ZOXMmAJ07d6ZPnz4MHTqUGTNmMH78eP7xj38wZswYypUrZ9WhiIjIRYqLCmXsgOYE2WyAwUshEyhjy4X6vaBRH6vLEz9iMwzDOP9mnmcYBlOnTuXbb7/l4MGDVK9enQEDBjBgwABsf3deS0tL44orruD555+nb9++AGRkZPD666+zYMECHA4HgwcP5q677iry66amphIVFUVKSopaf0REvExiSgYnVn1NgyWjILgs3L8cYmpbXZZ4AU99f1sWfKyi4CMi4sUyU+DD9pB2CLr9Ay5X3x4xeer722s6N4uIiLBwrBl6YupCl4esrkb8kIKPiIh4h4MbYeV/zOU+b0Kww9p6xC8p+IiIiHdY9CoYTmjSD+p2t7oa8VMKPiIiYr3Df8Aff1++fsUYa2sRv6bgIyIi1lvyjvmzUV+o1MjaWsSvKfiIiIi1knbDxqnm8mWPWlqK+D8FHxERsdbS98HIhTrdoFpbq6sRP6fgIyIi1jlxENZOMpcve8zaWiQgKPiIiIh1lv0bcrOgegeodanV1UgAUPARERFrnDwOq/9rLnd9HGy2c28v4gEKPiIiYo2Vn8GpNKjcDOr3tLoaCRAKPiIiUvqy0mDFx+byZY+qtUdKjYKPiIiUvjUTICMJYupAk+utrkYCiIKPiIiUrpws+O0Dc/nSR8AeZG09ElAUfEREpHStmwxpByGyGrS4yepqJMAo+IiISOnJzYGl75rLnUdCcBlLy5HAo+AjIiKlZ/N0c4qKsArQ5jarq5EApOAjIiKlw+mEJW+by53ugzLh1tYjAUnBR0RESse2H+HwFigTAe3vsboaCVAKPiIiUvIMA35901zucDeERltajgQuBR8RESl5uxbD/jUQXBY63W91NRLAFHxERKTk5bX2tLkNylWythYJaAo+IiJSshJWmS0+9mDzEnYRCyn4iIhIycq7kqvFYIiuYW0tEvAUfEREpOQc2gx//gDYoMsoq6sRUfAREZEStORd82eT66BiA0tLEQEFHxERKSmpB2DzNHP50kesrUXkbwo+IiJSMlaNA2cO1OgMVVtbXY0IoOAjIiIlITsT1ow3lzvea20tImdQ8BEREc/bOBVOHoOoeGjU1+pqRFwUfERExLMMA1Z8Yi63vxuCgq2tR+QMXvHbmJSURPny5c+7nWEY7N+/P9/6mJgYwsLCSqI0EREprt1L4NAmCA41R2oW8SKWtfgcOHCABx98kJiYGOrUqUNERASPPPIIp06dKnSf9PR04uPjadOmDZ06dXLdZs+eXYqVi4jIOeW19rS8icTsUH7bcZTElAxraxL5m2UtPvPnz6dx48bs3LmT6OhoNm7cSI8ePQgJCeH1118/577ff/89nTp1KqVKRUSkyJL2/D1gIcwJ78cDry7AaYDdBmMHNGdwe43cLNayrMXn1ltv5YEHHiA6OhqA5s2bM3ToUObMmXPefbOzszl27FgJVygiIsW28lMwnGTVuJwH5p7EaZirnQaMmbZJLT9iOa/q3PzXX39RpUqV827XvXt3ateuTXR0NE899RSZmZmlUJ2IiJxTVhr8PhGAnXVvcYWePLmGwe6jJy0oTOQ0rwk+M2bMYNasWYwaNarQbex2Oy+++CLHjh0jNTWVWbNmMX78eJ566qlC98nKyiI1NdXtJiIiJWD9V5CVAjF1iG7ZB7vN/eEgm41asboQRazlFcFnyZIlDB06lOeff54+ffoUul1YWBjPPvsskZGRAFx66aWMHj2azz//HKfTWeA+Y8eOJSoqynWLj48vkWMQEQloTies+I+53OFe4qLDGTugOUE2M/0E2Wy8MqAZcVGhFhYpAjbDMIzzb1Zyli1bRq9evXjooYd4+eWXi73/jBkzuP7660lMTCzwNFlWVhZZWVmu+6mpqcTHx5OSkuIKUCIicpG2z4MvB0KZCHh0C5Q1/74mpmSw++hJasWGKfTIRUlNTSUqKuqiv78tHcdn+fLlXH311YwcObLA0JM3bk/eOD1OpxO73Z7vOSIjI4mNjS3wNRwOBw6Ho0TqFxGRv+Vdwt76FlfoAYiLClXgEa9iWfBZs2YNvXr1YuDAgdx3333s27cPMPvxVK1aFYCUlBTi4+MZP348d9xxB++++y6HDx/muuuuIzo6mh9++IG3336b559/nuBgrxiLUUQk8BzdDn/NBWzQcbjV1Yick2Vp4eeffyYiIoKff/6Zn3/+2bU+KiqKzZs3A2YIqlatGuHh4QCMHDmSTz75hKeffpojR45Qp04dpk6dynXXXWfJMYiIBKLElAx2HU2ndmy42ZqT17enwdUQU8fa4kTOw/I+PqXNU+cIRUQC0ZRVexk9baNrUMI3r63JgIU9ITsdbpsBda6wukTxU37Rx0dERHxHYkqGK/SAOSjh1h8+huB0qNgYal9ubYEiReAVl7OLiIj323U03W1QQjtObrP/ZN7peC/YbAXvKOJFFHxERKRIaseGuw1K2MO+hnj7EZxly0OLwdYVJlIMCj4iIlIkcVGhboMSDgs2W3vsbW+HMhqRWXyD+viIiEiRDW5fg64NKnJ4+xpaztoCtiDocI/VZYkUmYKPiIgUS1xUKHH7vzbvNL4WoqpbW5BIMehUl4iIFE/6Mdg41VzudJ+1tYgUk4KPiIgUz5rxkJMJca0gvqPV1YgUi4KPiIgUXW42rBpnLnccoUvYxeco+IiISNH9+QOcOADhFaHZAKurESk2BR8RESm61f81f7a5DYId1tYicgEUfEREpGiO7YCdiwAbtLnd6mpELoiCj4iIFE1ea0/9q6B8TWtrEblACj4iInJ+2Rmw7ktzud1d1tYichEUfERE5Py2zICMJIiKN1t8RHyUgo+IiJxf3iXsbW8He5C1tYhcBAUfERE5t4MbYd9KsAdD69usrkbkoij4iIjIua0eb/5s1BciKltbi8hFUvAREZHCZZ2ADVPM5XbDrK1FxAMUfEREpHAbp8KpNKhQD2p3tboakYum4CMiIgUzDFj199g97YZpXi7xCwo+IiJSsH2r4dBGCC4LLW+2uhoRj1DwERGRgq3++xL2pgMgLMbaWkQ8RMFHRETyO3kcNk0zl9WpWfyIgo+IiOS3/ivIzYIqzaF6O6urEfEYBR8REXFnGKcnJFWnZvEzCj4iIuJu12I49heUiYDmN1hdjYhHKfiIiIi7vE7NLW4ER4S1tYh4mIKPiIgAkJiSwaqNWzD+mG2uUKdm8UMKPiIiwpRVe+ny6gJ++fptbM4cjpZvBVWaWV2WiMcp+IiIBLjElAxGT9sIhpObgxcAMPZwZxJTMiyuTMTzvCL4GIZRKvuIiEh+u46m4zTgCvs6qtmOkWSUY1ZuB3YfPWl1aSIeZ1nwSUpK4vnnn6d+/fo4HA5q1arFv/71L5xO5zn327VrFz179sThcFCuXDmGDRtGenp6KVUtIuJ/aseGY7fBLUHzAJiaezk5Nge1YsMsrkzE8ywLPlOnTsVmszFnzhxOnjzJhAkTeOONN3jppZcK3Sc7O5vevXsTGhrKgQMHWLt2LUuWLOHee+8txcpFRPxLXFQo710dwxX29QBMcV7JKwOaERcVanFlIp5nM7zonNHDDz/MokWLWL9+fYGPf//99/Tr14+9e/cSHx8PwNdff83QoUPZv38/VapUOe9rpKamEhUVRUpKCpGRkR6tX0TEZ81/EX59i+QqXci4+VuFHvE6nvr+9oo+PnkSExMpX758oY8vW7aMOnXquEIPQLdu3XA6naxcubI0ShQR8T+52bD2SwCiLxuu0CN+LdjqAvIsWLCAb7/9lsmTJxe6zaFDh6hYsaLbutjYWGw2G4cOHSpwn6ysLLKyslz3U1NTPVOwiIi/2PYTpB2EsFho2NvqakRKlFe0+Kxfv55Bgwbx4IMPMnjw4HNue3bn57wzdbZC5pIZO3YsUVFRrtuZrUUiIgL8/n/mz9ZDIbiMtbWIlDDLg8/GjRvp0aMHgwcP5t133z3ntnFxcRw+fNht3ZEjRzAMo9D+PaNHjyYlJcV1S0hI8FTpIiK+LzkBts81l9vcbm0tIqXA0uCzadMmrrzySgYOHMhHH31UYKtNZmYmubm5AFx66aXs2bOHnTt3uh6fP38+QUFBdOrUqcDXcDgcREZGut1ERORvaycCBtS6DCrUtboakRJnWfDZunUrV155JX379uWdd94hKyuLzMxMt/44ycnJhIaGMnHiRAB69uxJq1atGD58ODt27GDVqlU888wzDBs2jNjYWKsORUTEN+XmwNpJ5nLbOywtRaS0WBZ8Jk+eTEpKCpMnT6Z8+fJER0cTHR1NzZo1XdvYbDYcDgdBQUEABAUFMXv2bMqXL0+HDh3o168fAwYM4IMPPrDqMEREfNdf8yB1P4TGQONrra5GpFR41Tg+pUHj+IiI/G3yTbBtDlzyIPT6l9XViJyTX47jIyIipST1AGz/yVxWp2YJIAo+IiKBaO0kMJxQswtUbGB1NSKlRsFHRCTQOHPh9y/MZbX2SIBR8BERCTQ7FkJKApSNhibXWV2NSKlS8BERCTRrxps/W94EIZqXSwKLgo+ISCA5cRC2/Wgu6zSXBCAFHxGRQLLuS3DmQHxHqNzE6mpESp2Cj4hIoHA6Yc3fE5KqtUcClIKPiEig2PULJO8BRxQ07W91NSKWKHbwWbp0KT/++GOxHxMREYutmWD+bHEjlAmztBQRqxQ7+Cxbtox58+YV+tj8+fMvuigREfGwtCPwx2xzua1Oc0ng8tipLsMw2Lx5MxUrVvTUU4qIiKesnwzObKjWFqo0t7oaEcsEF3XDjz76iOeee46MjAwMw2DChAluj588eRKbzcaaNWs8XaOIiFwMwzh9mqvtHVZWImK5IgefK6+8kpiYGGbNmkVKSgpDhw51ezwyMpJ27dpRqVIljxcpIiIXYfevcHwnlCkHTQdYXY2IpYocfBo2bEjDhg3p3LkzWVlZ1K9fvyTrEhERT8m7hL35DeAoZ20tIhYrcvDJU6NGjZKoQ0RESkL6Mdj6vbmsTs0iGsdHRMSvrf8Kck9BXEuo2trqakQsp+AjIuKvDAN+//s0lzo1iwAKPiIi/mvvMji6DULCoNkgq6sR8QoKPiIi/iqvU3OzgVA20tpaRLyEgo+IiD86eRw2TzeX295pbS0iXkTBR0TEH234H+RmQeVmUK2N1dWIeA0FHxERf3N2p2abzdJyRLyJgo+IiL/ZtwoOb4HgUHPQQhFxUfAREfE3efNyNe0PodFWViLidRR8RET8SWYKbJpmLmvsHpF8FHxERPzJhv9BTgZUbAzxHayuRsTrKPiIiPgLwzg9dk/b29WpWaQACj4iIv7iwO9waCMEOaDFYKurEfFKCj4iIv4ir1Nzk34QFmNpKSLeSsFHRMQfZJ2Ajd+ay+rULFIoBR8REX+w8RvITocK9aFmZ6urEfFalgafP/74g1GjRhEXF0e3bt3Ou316ejrR0dH5bpMnTy6FakVEvFjeaS51ahY5p2CrXjgrK4v+/ftzzz330LNnTzZv3nzefQzDICUlhblz59KuXTvX+rCwsJIsVUTEux1YB4nrIKgMtBxidTUiXs2y4ONwONi6dSsAo0aNKta+5cqVIzo62vNFiYj4orx5uRpfC+EVrK1FxMv5ZB+fm266ibi4OC699FImTpxodTkiItbJSoMNU83lNrdbW4uID7CsxedC3XjjjTzyyCPExcUxZ84c7r33Xg4fPsxjjz1W4PZZWVlkZWW57qemppZWqSIiJW/zdDh1AmLqQK3LrK5GxOv5VPApV64cU6ZMcd0fMWIEBw4c4OWXXy40+IwdO5YXXnihtEoUESldeZ2a29wOdp9sxBcpVT7/KWnXrh3JyckcPny4wMdHjx5NSkqK65aQkFDKFYqIlJCDm2D/arAHQyt1ahYpCp9q8SnIH3/8gcPhIDIyssDHHQ4HDoejlKsSESkFeZ2aG/WBcpWsrUXER3h1i09KSorbOD3//e9/mTRpEikpKeTm5vLjjz/y6quvcuedd1K2bFmLqxURKUWnTsL6v0/9q1OzSJFZ2uLTuXNntmzZQkZGBrm5ua5L1Pfv3094eLhr3J5Tp04B0Lt3b/75z38yatQo0tLSqFy5Mo8//jiPP/64hUchImKBLTMgKwWia0Cd8w8AKyImm2EYhlUvfuLECXJzc/OtzwtAecEnLCyMMmXKuG2TnZ1NSEhIsV8zNTWVqKgoUlJSCj09JiLi9cb1goTl0P1Z6Kr//In/89T3t6UtPhEREed83GazFTpQ4YWEHhERv3B4qxl6bEHQ+harqxHxKV7dx0dERArw+xfmz4bXQEQVa2sR8TEKPiIiviQ7E9b9PTFz2zssLUXEF/n85ewiIv4uMSWDXUfTqR0bTtyemZCZDFHxULe71aWJ+BwFHxERLzZl1V5GT9uI0wAbsKjiR9QEaH0r2IMsrk7E9+hUl4iIl0pMyXCFHoDatgPUPLGWXMPG93Zdwi5yIRR8RES81K6j6a7QA3BT0EIAFjpb8cicoySmZFhUmYjvUvAREfFStWPDsdvM5TJkMzBoMQBf5XYn1zDYffSkhdWJ+CYFHxERL5KYksFvO8zWnLioUMYOaI4d6GlfTQXbCQ4a5VnkbEWQzUat2DCryxXxOercLCLiJc7syGy3wdgBzRncvgZdG1Qk6/O34ARMyb0CbMG8MqAZcVGhVpcs4nMUfEREvMDZHZmdBoyZtomuDSoSdyoBTqzBsNm59MZHubFmfYUekQuk4CMi4gXO7sgMuPrxxG2fAICtfi/atmhR+sWJ+BH18RER8QJndmTOE2SzUSvaBuv/Hqm53bDSL0zEzyj4iIh4gbyOzEE2M/0E2WxmP559P0NGEkTVgHpXWlyliO/TqS4RES+R15F599GT1IoNM/vxfH6X+WDb2zRSs4gHKPiIiHiRuKjQ0x2XD26EfSvBHgytb7O2MBE/oVNdIiLeatU482fjayGisrW1iPgJBR8REW+UmQob/mcut7vL2lpE/IiCj4iIN9owBbLTIbYh1LrU6mpE/IaCjxTZmUPp+/NriljOMGD1f83ldsPAZjv39iJSZOrcLEVS2FD6npCYksGuo+nUjg13G422JF9TxKvtXQ6Ht0BIGLS8yepqRPyKWnzkvAobSt8TrTBTVu2ly6sLGPLZCrq8uoApq/aW+GuKeL3Vf3dqbjYQQqMtLUXE3yj4yHmdayj9oirolNW5wo0nXlPEJ6Udgc3fmcvt77a0FBF/pFNdcl55Q+mfGUSCbDZqxYa5bVfcU1bnCjfne83CXkvE5639ApzZUK0dVG1ldTUifkctPnJehQ6lf1a4Ke4pq0LnJvp7xNrCXrOw1xLxec5cWD3eXG6vS9hFSoJafKRIChxK/2+FhZuuDSqes1XnkroVGDugOWOmbSLXMPIFqoJe81yvpZYf8XnbfoSUBAiNgaYDrK5GxC8p+EiRuQ2lf4aLOWV1rkBV0Gue67UUfMTnrfzM/NnmVggpa20tIn5Kp7rkol3oKas8cVGhXFK3QpGCy7leS8SnHf0Ldi4EbBqpWaQEqcVHLlpeuCnOKSu4sA7K53stEZ+16nPzZ4OroXxNa2sR8WM2wzCM82/mP1JTU4mKiiIlJYXIyEiry/EriSkZhZ6yOtvFDk5YnNcS8XpZafB2Y8hKhVu+hXo9rK5IxOt46vtbLT7iMYX1ATqbJzooF/W1RHxBysovicpKJSe6NsF1ultdjohfUx8fKXUanFDktCkrd3N47nsAvHq0C1PW7LO4IhH/ZmmLz8GDB/n888+ZMmUK9erVY/r06efdJzU1leeff5758+dTtmxZBg8ezKhRo7DbleG81snjcHAjnDwKudk0SUvnlqBNBJGLDYNkoxw7jTg2763NJXUrWF2tSKlJTMlg/oz/Y3DIflKNML7OuYIMDc8gUqIsCz6nTp2iQ4cO3HLLLTRp0oQdO3YUab/rr7+e5ORk3nvvPZKSkrj77rs5evQor7zySglXLEVy4iAkrj/jtgFS3AcYjAZeDsm/a86if5K9uREh1dtAXEvzVqUZlAkvldJFStuuI2ncGzQTgEm5PUgjDDQ8g0iJsiz4hISEsGPHDkJCQhg1alSRgs+iRYtYuHAhmzZtomnTpgAcPnyYhx9+mKefflqdla1y4iCsmwxrJ8HxQt7H8rUgsjoElwF7CMcyDX7bnYoNiCGVhvYEKthOwNEt5m3dJHM/mx1qXAKthkKTfuAoV1pHJVLiGmRtIta+nSwjhPE5VwMankGkpFkWfGw2GyEhBfy3/xwWLVpEtWrVXKEHoHfv3owYMYJly5bRq1cvT5cphcnNgb/mwu9fwLafwMg119vsENsQ4lr83WLTAqo0zzfD9KmUDB5+dcEZfX0MqtuS+P6GcsQkbzndYpR2EPYsNW9znoSm10PrWyG+I9jOGtBHxMfErvsYgGnOrhwhWsMziJQCn7qqKyEhgSpVqrity7u/b1/BHQKzsrLIyspy3U9NTS25AgPBsR1my866yWYoyRPfyRxttkk/cESc92nyj8djZ+SAy4lpUwPof3rD5L2w4X/maybtMn+unQQV6sGlj0KrIQpA4psObYHtPwE2rrzrJb7KraLhGURKgU8FH6fTma+VKCgoCLvdTm5uboH7jB07lhdeeKE0yvNvx3bAz/+AP384vS4sFlrdbLbAVGxY7Kc833QVAETXgK6Pw2WPwZ7fzNCz5Ts49hfMuB92zIe+70JZneYUH7PUvJKLJtdRqVZTKllbjUjA8KngExsby7Fjx9zWJSUl4XQ6iY2NLXCf0aNH8+ijj7rup6amEh8fX6J1+qoCR1LOOgGL34BlH4EzG7CZg6u1uRUaXGP22bkIRR6Px2aDWl3MW+/XYcUnsHAsbPoW9v8ON4yHqq0vqhaRUpOcAJu+MZe7jLK0FJFA41PBp3379rz99tscPnyYSpXM/x8tWbIEgHbt2hW4j8PhwOFwlFqNvirfSMr9mzK4zDKY909IO2RuVK8H9BoLFRtYW6wjAro+AbW6wrd3mafAPr8Ker4MHe/VqS/xfsv+Dc4cqN0VqrWxuhqRgOLVg9+cOHGCevXqMW3aNAD69u1LXFwcY8aMIScnh5SUFF5++WV69+5NjRpFn+5A3J09knIzdtBg1kD4boQZemLqwM1TYOg31oeeM9XoCPcuhkZ9zdaoH5+Cr4ea4waJeKuTx+H3/zOX1dojUuosbfHp3bs327Zt4+jRo2RmZlKvXj0ANmzYQFhYGLm5uezYscPVITksLIyZM2cydOhQYmJiyMrKomvXrkyYMMHCo/B9eSMpVySZJ4KncGPwLwDkBocTdMUT0Ol+CD5/q9mFTDp60cJiYPAkWPnp332QZsMn62HQf81gJOJtVn4G2SfNKx7rFjw9hSWfJZEAYekkpQkJCW5XXOWpW7cuNpsNp9PJzp07qVy5MhER7lcKJSYm4nA4iImJKdZrapLS/BJTMnj29Td5K/gjomzmtBHTci+jy4gPqFytdpGe42InHfWIA+vgmzvh+E6wBUH3f5j/o9ao3uItTp2Ed5vByWMwcBw0H5RvE6/4LIl4IU99f2t29kDnzIVFr8Li1wHY6KzF8znDuLF//yL/sU1MyaCL25g85iBsS57uVvr/W81MhVmPnO44Wrc79P8PlNM1M+IFVnwKc56A6Jow8ncIcm9096rPkoiX0ezscvFOHodv7zYvCQfSWw0jvcmTfFg5ulh/ZM816Wip/7EuGwkDP4c6l8MPT8KOBfDJpTDgM3OdiFVyc2DZB+Zy55H5Qg942WdJxE/pHECgOrAOPr3cDD3BodD/U8Kvf4dODeKK/Qe2dmw49rMupLJ02H2bDdrcBsMXQsVGZgftL/rBgn+ZXz4iVtg83RyQMywWWt9S4CZe91kS8UMKPoFo7SQY19P8I1y+Ntw9D1oOvuCnyxuFOejvy8i9Ztj9So3hnoVmCMIwT+d9cR2kHrC2Lgk8hnF6wMKOIyCk4M+G136WRPyI+vgEkpwsc76rNRPM+w2ugf6f5JtH60IlpmScexRmK238BmY+DKfSIDTGPO4GmttNSsn2efDlQAgJh0c2mVcjnoNXf5ZELKI+PlI8yQnwv9vgwO+ADbo/A5c+5tErnoo8CrMVmg8yR3aeegcc3ACTb4RLHoQr/3nRo0+LnNfSd82fbe84b+gBL/8sifg4neoKBDsWwn+6mqEntDzc8o058nGgXeZdoa55Wq/Dveb9ZR/C+KshabelZYmf27cGdv8K9mC45H6rqxEJeAH2zRdgnE749S2YNAAyjkNcKxj+izn1RKAKdphzfQ3+EspGwf418ElX2DLD6srEXy19x/zZ/EaIqm5tLSKi4OO3crNh2j0w/0UwnOYM6sN+gvI1ra7MOzTuCyOWQPUOkJVingac9ShkZ1pdmfiTo9th6yxzucvD1tYiIoCCj3/KyTL7smz6Bqc9hOQeb0K/DyGkrNWVeZfoGnDnD3DpI+b91ePg8x7ml5WIJ/z2PmCYFxJUamR1NSKCgo//yc4wJ+r8YxZZRgjDMkfRZnZVpqzaa3Vl3ikoBHo8D7d8a46vcmgj/LeXwo9cvBMHYf3X5vKloywtRUROU/DxJ1lp8OUN8NdcThoO7sx+gkXO1jgNGDNtE4kpGVZX6L3q9YD7lpr9oE4eg4kDIDXR6qrEly3/CHJPQXwnqNHJ6mpE5G8KPv4iMwUmDYTdv5ITUo7bTz3Fb85mrofzhr2Xc4ioYrb8xNSFlL1/dwpPsroq8UWZKbB6vLms1h4Rr6Lg4w9OHocvroeE5VA2iuRB/2MN7v0JNOx9EYXHwq3ToVwVOLwFvrrZPH0oUhyr/wtZqeaUKfU1UKaIN1Hw8XVpR+D/rjXH6AmrALfPIrZhFw17fzHK1zRbfhxRsHcZfDNMc3xJ0WVnwvKPzeUuDwfeeFkiXk4jN/uy1ERz8s2jf0K5ynDbDHN+KmBw+xp0bVBRw95fqCrN4OavYGJ/+PMHmPUwXPehOQGqyLms/8qcGDeyGjQbZHU1InIW/VfEVyUnwITeZuiJrAZ3/OAKPXniokK5pG4FhZ5iSEzJ4LcdR82O4LW6wA3jwWY3J3ad/6LV5Ym3c+b+fQk7cMkDmg5FxAupxccXHd8J/9fP7IAbXRNu/x7K17K6Kp83ZdVeRk/biNMAuw3GDmjO4PZ9oO+7MPMhWPI2lKsEne6zulTxVltnmp/PstHQ5narqxGRAqjFx9cc2Qbje5uhp0I9uHOOQo8HJKZkuEIP4D4EQNvbofuz5gM/Pg0bplpXqHgvwzg9GWmHe8BRztJyRKRgCj6+5NBm8/TWiUSo2Ng8vRVVzeqq/MKuo+mu0JPHbQiAyx6DjiPM5e9GwF/zSrdA8X67FsOBtRBc9vREuCLidRR8fMWBtTChD6QfgSot4I7ZEFHZ6qr8Ru3YcOxn9Vt2GwLAZoNeY83Oqs4cmHKbOeu2SJ681p7Wt0C5ipaWIiKFU/DxBQkrzT49GUlQrZ3Zpye8gtVV+ZW4qNDzDwFgt8P1H0Pd7pCdDl8OMk89iiSuhx0LzI7wlzxodTUicg7q3Oztdv0KkwebX7Q1u8CQKeCIsLoqv1SkIQCCy8CNE0+PnTRpgDnrvU45Bral75k/mw6AmNrW1iIi56QWH2+261dz7q3sdKhzBQz9RqGnhBVpCABHORg61excnpJgThVy8njpFSne5fgu2DzdXO7ysLW1iMh5Kfh4q73LzZaenAyo3xNungJlNOWE18ib2iIiDo5sha9uglOaCy0QuI31BLDsQzCcUPdKiGthbXEicl461eWN9q2BSYPMlp663c1TKyFlra5KzhZdA26ZBuOvhoQV8M2dMHgSBIVYXZmUkLPHenq7T1WuXzvJfFCTkYr4BLX4eJvEDTCpP5w6AbUug8FfKvR4s8pNzNa44LKw7UdOfns/v/115HRrgPiNgsZ62vfje5CTCVXbmJ9XEfF6Cj7e5PAfMPF6yEyB+I5w89c6veULal4Cg8bjtAURtuV/rBj/FF1eXcCUVXutrkw86OyxnsLI5Jagn807l47SPG4iPkLBx1sc2wFfXAcnj0HV1mbnWY386jMS47ox5tQwAB4J+ZZethWukZ/z9QkRn3T2WE83By0g2pZOTnQdaNTXusJEpFgUfLxB8l74v+vMGZ0rNTX7jZSNsroqKYZdR9P5Orcbn+X0BuDtkI9pxC7GL9lNl1cXMOSzFWoF8nFnjvUUQg53B/8AQPBlD4M9yOLqRKSoFHyslnrAHBMmdR9UqA+3fQdhMVZXJcWU1xowNmcIi3JbEmo7xWdl3uS7Jb8XPP+X+KTB7Wuw5OluzOl+iDjbcShXGVrcZHVZIlIMCj5WSjsCX/SDpN2nZ1kvV0mnRnxQXFQoT13TCGx2RmaP5C9nVarajvNJyNs4OOXazm3+L/FJcREO6m0fZ97pdJ8uPhDxMZZezp6VlcV7773H/PnzKVu2LIMHD2bIkCGFbp+RkcFVV12Vb/0zzzzDNddcU5Klet7J4zCxPxzdBpHVzNATWTXf5bJjBzRncPsaVlcr5zFl1V5em/MHTgPSCGPVJR9RZ90dtMn6i1dCxvFY9gjA5j7/l/im7T/BkT/AEQnthlldjYgUk6XBZ8iQIWzYsIGXXnqJpKQk7rnnHvbv388TTzxR4Pa5ubksXbqU//znPzRp0sS1vkGDBqVVsmdkpphTHRzaCOGV4LbvoXytAi+XHTNtE10bVDz3SMJiqbPfNwP4x+IMeg75jPLTbmJg0K/86azOOOd1+ef/Et+z5F3zZ7s71RdPxAdZFnxWrFjBtGnTWLFiBR06dAAgMzOT5557jgceeICwsML/V9yiRQs6depUWqV6VlaaOQ3FgbUQGgO3zYDYekD+y2Xh9KkRfVl6r8Let21hbbnkmtfgh8cZHfI1I/r1Jqa1Wu982p5lkLAcgspAp/utrkZELoBlfXzmzp1L5cqVXaEHoF+/fqSlpbFs2bJz7jtmzBiuuuoqRowYwZo1a0q6VM/JzjCnNkhYYf5P8bbvzAHw/nb25bKATo34gHO+b+3vhnbDsGEQ8+MDcORPa4qUi+d0ws//MJdb3gwRVaytR0QuiGXBZ8+ePVSr5j6jdd79vXsLv+S3devWDBkyhFGjRhEcHEynTp345ptvCt0+KyuL1NRUt5slcrJgyi2w+1coE2Fesh7X0m2TMy+XBfPL88mrG7LraLo6Onuxgt431yktmw2ufg1qdIasVPjqZshIsrhiuSAb/wf7V0OZctBtjNXViMgFsuxUV3Z2NmXLul8NUaZMGex2O6dOnSpwn7CwMJYvX06ZMmUA6NOnD7m5uTzyyCMMGjSowH3Gjh3LCy+84Nniiys3G6beCX/Ng5AwGPo/qN6uwE0Ht69B1wYV2X30JBv2J7s6zKqjs3c7832rFRvmfmoyuAzc+AV81g2O74Bv7jIHqNTYL74jKw3mPW8uX/aYWntEfJhlLT4xMTEcO3bMbV1ycjJOp5OYmILHsbHb7a7Qk6dnz57s27cv33PlGT16NCkpKa5bQkKCZw6gqHJzYNo98OdsCHLATZOhZudz7hIXFUqt2DBX6AGNAeML4qJCuaRuhYL7Y5WraL73waGwYz7Mfa70C5QLt+QdOJEI5Wupb4+Ij7Ms+LRu3ZqdO3eSnJzsWrdy5UrXY0V16NAh7HY7DoejwMcdDgeRkZFut1KTmwPTh8Pm6WAPgcEToW4318PnGq/nXB2dxUfFtYDrPzKXl30Iq8dbW48UTdJu+O0Dc7nnvzRuj4iPsyz49OvXj8jISMaOHQuYp75ee+01LrvsMurVM69ySktL49JLL2XOnDkAzJw5k7Vr17qeY+fOnbz++uv06dOHcuW8bF4rZy58NwI2fWuGnhu/gAa9XA9PWbX3nFMZqKOzn2o2AK4YbS7Pfgy2/WxtPXJ+c5+D3Cyo3RUa9bG6GhG5SJYFn8jISKZOncqECROoXbs2VatW5ejRo0ycONG1TU5ODkuXLuXQoUMA1KpVi5EjR1KjRg1atGhBkyZN6NSpE+PHe9n/nJ258N19sHEq2IPhhgnQqLfr4cLG6zmz5eecHWbF57i17l3+FLQaCkYuTL0DDqyzujwpzO4lsGUG2Oxw9auagV3ED1g6gGG3bt1ISEhg8+bNOBwOGjdujO2MPywRERH8+uuvrgEKmzdvzpIlSzhw4ABHjhyhdu3apXvqqiicuTDjAdgwBWxBMGg8NHafubmo4/Wcs8Os+IwCR+O+9j1I3Q87F8HkG+Hu+RAdb3WpciZnLsx52lxuNwwqN7W2HhHxCEuDD5hXchXWpycoKIhLL7003/qqVatStWrVki6t+JxO+H4krP/q79DzX2hyXb7N8k5jnRl+CjuNFRcVqsDjwwpq3Rv97UbCHcG0u/pTqnzTHw5vNge1HPYjhEZbWq+c4fcvzNHVy0bBFbp8XcRfaJJST3E6YeZIWPelGXoGfg5Nry9wU53GChwFte45gQcnr6XzO6v5vtm7EBEHR7aa4zzlFDyUg5SyjGRY8JK5fMUYCK9gaTki4jmWt/j4jcVvwNpJZl+AAZ+anVjPQaexAkNBrXt5nAY8MucolwybSMWp15uDW34/Evp/or4kVlv8Bpw8BrENof1dVlcjIh6kFh9PaXcnVG4G/T+F5u6DKRZ22fo5x30Rv3B2697Zcg2Dv+x14Mb/M1sKN3wNi8aWcpXi5uh2WPGJuXz1KxAUYm09IuJRavHxlHKVYPgvEOT+T1pgx1aNvhxQ8lr31uxO4qGv1xbctyuqB1z7rtni88trEBUPbW61rOaA9tMYcOZAg6uhXg+rqxERD1OLjyedFXqKctm6BIa4qFD6tqx67r5dbW6Dyx43l2eNgh0LrCk2kG2fC9t/Nsfe6vkvq6sRkRKgFp8SVNTL1iVwnLdvV/d/QEqCORzClNvMK72qNLOm2ECTm2229gB0vBdi61lbj4iUCLX4lCCNviwFOWffLpsNrvsQal0Gp06Yl7mn7C/9IgPRqs/h6DYIi4XLn7S6GhEpIQo+JUiXrcsFCS5jzusW2xBOHDAHOMxMtboq/5Z+FBb+3an8yufMsXtExC/ZDMMo4EJb/5WamkpUVBQpKSmlNupzYkqGLluX4kveC5/3gLRDULc7DPmfrjAqKTNHwZrxUKW5eZGCPcjqikTkLJ76/laLjwfpsnXxqOgaMGQKhISZHZ1njYLA+n9K6dizzAw9wLHLXlToEfFzCj4ecr7Z1kUuSNXW5nxvNrs5QObiN62uyL9kZ5D6vxEAfJ1zBe0nZeizK+LnFHw8QJetS4lqeDX0/jvwLHwZ1k+xth4/kvbTS0Sm7+agUZ5XcobqsysSABR8POBcl62LeET7u6DLw+byjAdg5y/W1uMPElYRvuZjAP6RPYxUwgF9dkX8nYKPB+iydSkVVz4PTQeAMxum3AqHt1pdke86lQ7Th2MznHyX24V5zrauh/TZFfFvCj4eoMvWpVTY7XD9x1DjEshKMcf4OXHQ6qp809zn4PhOiKiK85rX9dkVCSC6nN2DdNm6lIqTx2HcVXDsL4hrCXf8AI5yVlflO/6aB5MGmsu3fgd1u+mzK+IDdDm7F9Jl61IqwmJg6DfmCMOJ6+GbOyE3x+qqfEPKPphuXsVFh3uhbjdAn12RQKLgI+KLYmqbY/wEh5qTas55QmP8nE92Bnw9FNKPkF6+EYkdnrK6IhGxgIKPiK+q3g4GjQNssPq/sPRdqyvyXoYBMx+GxHUcN8rR6+B9dHlrucbsEQlACj4ivqxRH7j6VXN53vOw8RtLy/Fayz+CDVPIMew8kP0w+4yKGrNHJEAp+Ij4uk4joNMD5vJ398Ge36ytx9vsWAg//wOAl3NuYZmzqeshjdkjEngUfET8Qc+XofG1kHsKvroZjmyzuiLvcHyX2fnbcHKyyY184ezl9rAdOJaepVYfkQCi4CPiD+x2GPAZVG8Pmcnw5SBIO2x1VdbKSoOvh0BGElRrS1j/Dxg7oIVrzB4bYAAPTl6r+fVEAoiCj4i/CAmFm7+G8rUheQ9MHmyOUByIDMM87Xd4C5SrDIMnQUhZBrevwZKnu/Hhza2x2czgA5pfTySQKPiI+JPwWLjlWwiNgQO/wzfDICfL6qpKTWJKBr/tOMqJuWNh6/dgD4EbJ0JkVdc2cVGhxJQro/n1RAKUgo+Iv6lQ12z5CXLAth/Nlp+sNKurKnFTVu2ly6sLGDfuIyJ+e81c2ectqNEx37aaX08kcCn4iPijGh1h6P8gJBx2LoSJ15t9XfxUYkoGo6dtpDb7eTfkIwAm5l5FYr0bC9xe8+uJBK5gqwsQkRJS5wq4/XtzXqp9q2B8H7h1OkRUtroyj9t1NJ0Oti28E/IREbYMVjgb8UL2rdQ7erLQMDO4fQ26NqioObpEAoxafET8WfV2cOccKFcFDm+GjzrCknf8q9NzbjYt/niPySH/Is52nB3OOO4/9TCGLeS8p640R5dI4FHwEfF3lZvAsDlQsbF5umve8/BeS1j2b3P+Kl92bAeM60m5Ve9jtxlMye1G31P/ItkWrVNXIlIgm2EE1syGnprWXsTn5ObAxqnwy6uQtNtcFxEHlz0GbW6DYIel5RWLYcD6r+CHJ+BUGpSNhuveJ7FaT526EvFTnvr+tjT4OJ1OJkyYwPz58ylbtiw33ngjvXr18vg+Z1LwkYCXmw3rJsPiNyAlwVwXFQ9dn4BWQyAoxNr6zicjGWY9ApunmfdrXgoD/gNR1S0tS0RKlqe+vy091XX33Xfz7LPPcskll1CrVi2uu+46Pv30U4/vIyJnCAqBtrfDyDXQ+02z/09KAsx8CD5sB+u+Ameu1VUWbM8y+ORSM/TYgqD7s2YHboUeESkiy1p81q9fT6tWrVi0aBGXX345AP/617946623SExMxOHI3+x+IfucTS0+ImfJzoDV/zU7PacfMddVqA9XPA1NB5jTYVgtNwcWv262UhlOKF8LBo4zO2+LSEDw+RafOXPmEBsbS9euXV3rbrjhBpKSkli+fLnH9hGR8wgJhUsegIfXQ4/nIbQ8HNsO395ltq5snWn2qbFK0m4Yfw388poZeloOgRFLFHpE5IJYFnx27dpF9erVsdlOD59ao0YN12Oe2icrK4vU1FS3m4gUoEw4XPoIPLwBrhgDjkjzEvgpt8Cnl8O2n0o/AG2YCp9cBvtWmvUMHAf9PwZHROnWISJ+w7Lgk5WVRViY+xgbZcuWJSgoiMzMTI/tM3bsWKKioly3+Ph4zxyAiL8qGwlXPAWjNsBlj0OZcpC4HibfCJ/3gB0LSj4AZabCtOEw7W7ISoX4TmYrT/NBJfu6IuL3LAs+UVFRJCW5D6GfnJxMbm4u5cuX99g+o0ePJiUlxXVLSEjwzAGI+LvQ8nDls2YLUOeHIDgU9q+Gif1hQh/YvbTIT5U3eWiRZj9PWGWeYtswBWx2uGI03DEbyte8iIMRETFZFnxatmzJzp07OXHihGvdunXrAGjRooXH9nE4HERGRrrdRKQYwitAz5fMPkAdR0BQGdizFCb0hi/6mUHlHPImDx3y2Qq6vLqAKav2FryhMxd+eQP+2wuS90BUDXPU6SuehiDNriMinmFZ8OnXrx9lypThgw8+AMzxed566y3atm1L48aNAUhPT+f6669nwYIFRd5HREzFamUpiojKcM1r8NA6aDcM7MGwcxGM6wFf3gAH1hZYw+hpG3H+fWbMacCYaZvy15ScABP6wsKXwciFZoPgviVQo5NnahcR+Ztl/42qUKECEydO5NZbb2X69OkkJydz6tQp5syZ49omOzubGTNmcP311xd5HxExW1nyAofdBmMHNGdw+xqeefKoatD3HejysNlCs/4r2P6zeWvUF7qNgcpNAXPyUOdZ3YFyDYM9h5KIS91knjrbtxq2z4WsFCgTAX3ehBaD4YyLGEREPMXyKSuSk5NZtWoVDoeDTp06UaZMGddj2dnZzJ49mzZt2riu3jrfPuejcXzE3yWmZNDl1QVugSPIZmPJ091KZhqHYztg0avmdBgYgI2MBtexo+YNOByhPD1tI4YBNWyHaWX/i9b2HbQI3ovNme3+PNXawcDPIaa252sUEZ/nF1NWWEHBR/zdbzuOMuSzFfnWf3VPJy6pW6HkXvjwH7BoLGz5rmjbh1WA6u3NwFO9HdS6TH15RKRQnvr+1l8ZET9TOzYcu418LT61YsMK38kTKjUisdcn3L2uAw8EfUcz2y4MbGCDKpFlOeWogD2+HeF1OkG1tuboyzqdJSKlTMFHxM/ERYUydkBzxkzbRK5hEGSz8cqAZoDZGlQ7NrzEZi7fdTSdzc5a3O8c5bb+q+tLuLVJRKSIFHxE/NDg9jXo2qAiu4+epFZsGIu3HXH1+/F4Z+czWNbaJCJSRF4w+6CIlIS4qFBXK0uRLin30GuOHdCcoL9PYeW1NpVUC5OISHGpxUfEzxV2SfnuoydLJJCc3dpUlNdITMlg19H0Ej0NJyICCj4ifs+K009xUaFFDjAlOuaQiMhZdKpLxM958+mnIo/sLCLiIWrxEQkAF3L6qTSU9mk4EREFH5EAUZzTT6VFV4GJSGnTqS4RsYw3n4YTEf+kFh8RsZS3noYTEf+k4CMilvPG03Ai4p90qktEREQChoKPiIiIBAwFHxEREQkYCj4iIiISMBR8REREJGAo+IiIiEjAUPARERGRgKHgIyIiIgFDwUdEREQChoKPiIiIBAwFHxEREQkYATdXl2EYAKSmplpciYiIiBRV3vd23vf4hQq44HPixAkA4uPjLa5EREREiuvEiRNERUVd8P4242Kjk49xOp0cOHCAiIgIbDZbkfZJTU0lPj6ehIQEIiMjS7hCawXSsUJgHa+O1X8F0vEG0rFCYB3v+Y7VMAxOnDhB1apVsdsvvKdOwLX42O12qlevfkH7RkZG+v0vXp5AOlYIrOPVsfqvQDreQDpWCKzjPdexXkxLTx51bhYREZGAoeAjIiIiAUPBpwgcDgf//Oc/cTgcVpdS4gLpWCGwjlfH6r8C6XgD6VghsI63tI414Do3i4iISOBSi4+IiIgEDAUfERERCRgKPiIiIhIwAm4cn8zMTN5++23mz59P2bJlGTx4MLfddttF73Mhz1safvzxRz777DOOHz9Ou3btGD16NDExMYVun5iYyMcff8zKlSsJCwuje/fuDB8+nDJlyri2GTNmDAsWLHDbr02bNnz00UcldhxFsW/fPl555RW2bNlCXFwcI0eOpHPnzoVuv3jxYp588sl862fOnEnFihUv+HlLQ25uLh999BGzZs0CoG/fvtx///0EBQUVuP3SpUt57LHHCnzso48+ok2bNgBcddVVrtHN84wYMYI77rjDc8VfgFWrVvHxxx+zZcsW3n33XTp16nTefX777Tc++OADEhMTadKkCWPGjMk3hldRtiltR44c4b///S8zZ86kW7duvPTSS+fc3jAMvvvuO6ZPn86BAweoV68eI0eOpGnTpq5ttm/fzq233ppv308//ZQWLVp4/BiKKjc3l1mzZjFu3DgOHz7M4sWL3f7WFOS2225j27Ztbuv69evH6NGj3dZNmjSJr7/+moyMDLp168Zjjz1GaGiox4+hOLZu3conn3zCypUrefLJJ+nfv/85t7/zzjvZunVrvvVn/r2dMmUK77zzjtvjwcHBLFmyxHOFX4B58+bx1VdfsXv3bmrWrMm9995Lx44dz7nPyZMnefPNN1m0aBHh4eHcfPPNDBkypNjbnJcRYPr162c0aNDAmDp1qvHpp58a5cqVM8aOHXvR+1zI85a0b7/91ggODjbGjh1rfP/990aXLl2M5s2bG1lZWQVuf/ToUaNOnTrGSy+9ZMyZM8f48ssvjdq1axt9+/Y1nE6na7uBAwca/fv3N5YtW+a6bd68ubQOq0DHjx83qlevblx77bXG7NmzjSeeeMIICQkxli5dWug+06dPN0JDQ92OY9myZW7/PhfyvKXh/vvvN6pUqWJMmjTJ+PLLL41KlSoZDzzwQKHbJyUl5TvOm266yYiMjDTS0tJc20VFRRmvvPKK23b79u0rjUMq1Isvvmi0bdvWePPNNw3AmDNnznn3Wbp0qRESEmI8+eSTxuzZs42+ffsa8fHxxvHjx4u1TWn766+/jGrVqhmPP/640aFDB2Pw4MHn3efRRx81BgwYYHzxxRfGvHnzjPvuu88oU6aMsXz5ctc2a9euNQDjxx9/dHtvU1NTS/Jwzqtnz57Gtddeazz00EMGYGRkZJx3n5YtWxojR450O44dO3a4bfP6668b4eHhxieffGJ88803RsOGDY0+ffqU1GEUyRdffGE0atTIePvtt43Q0FDj448/Pu8+GzdudDvOn376ybDb7cZLL73k2uadd94x6tSp47bdme+9Fd58802jR48exrhx44z58+cbY8aMMex2uzFjxoxz7terVy+jSZMmxrfffmt89NFHRmhoqPHOO+8Ue5vzCajg89tvvxmAsWrVKte6d955xwgPD3f741/cfS7keUtDw4YN3b4MDx8+bISEhBgTJkwocPvs7GwjMzPTbd38+fMNwPjzzz9d6wYOHGjce++9JVP0BXrxxReNihUruoWWPn36GD169Ch0n+nTpxvh4eEef96Stnv3bsNutxvTp093rZs6dapht9uNvXv3Fuk5cnJyjGrVqhkjRoxwWx8VFWVMnTrVk+VetOTkZMMwDOPIkSNFDj7du3c3+vXr57qfmZlpVKhQwXj55ZeLtU1py8jIcH0G+/TpU6Tgc+LEiXzrLrnkEuPWW2913c8LPkeOHPFcsR6Q995OnTq1WMHnjTfeKPTxkydPGuXKlXP7Mly1apUBGEuWLLnomi9USkqKazk8PLxIwedsn3zyiREUFOT2n5F33nnHaNq0qUdq9JSCfidvuOEGo1u3boXus2DBAgMwNm7c6Fo3duxYIyoqyvWZKMo2RRFQfXzmz59PlSpVaNeunWtdv379SE9PZ/ny5Re8z4U8b0nbt28ff/75J9dee61rXcWKFbnkkkuYN29egfsEBwfnGz+hXLlyAJw6dcpt/fz587niiiu44YYb+Pjjj8nNzfXwERTP/Pnz6dWrl1szeb9+/fjll1/Izs4udL+srCz69OlDr169ePzxx0lMTPTI85akhQsXYrfbufrqq13r+vbti81my3cKsjA//fQT+/fv55577sn32JtvvskVV1zB7bffzs8//+yxui9UcYeoz87O5tdff3X73Xc4HPTq1cv1u1+UbaxQtmzZYo9hkvcZPXvd2Z9ZgFtuuYUePXrwwAMP5DtdZIULnX5g4sSJXH755QwZMoQpU6a4PbZixQrS0tLc3tt27dpRtWpVS99bT0w3MW7cOHr37k21atXc1u/fv5+ePXvSp08f/vnPf5KSknLRr3UxivM7mWf+/PnUrFmTZs2audb169ePlJQUVq9eXeRtiiKggs+ePXuoWrWq27q8X6A9e/Zc8D4X8rwlLe91z66ratWqxarplVdeoUGDBjRu3Ni1Ljo6mltvvZVnnnmGHj168PLLL9OvXz/PFH6BCnoPqlatSnZ2dr4wk8dmszFo0CDuuece7rvvPjZt2kSTJk3YtWvXRT1vSduzZw8xMTGULVvWta5s2bKUL1++yO/tuHHjaNOmjatvT55GjRpx55138swzz1CjRg2uu+66fP0HvN2BAwfIzs4+5+9+UbbxVcuXL2f+/Plcf/31but79uzJnXfeySOPPEJycjItWrRgxYoV1hR5EapVq8Ydd9zBs88+S+vWrbn33nsZNWqU63FP/e3zNhs3bmTVqlX5/rMSFBTEzTffzKhRo7j99tuZM2cOLVu2JDk52ZpCC/DXX38xZcqUfL+TZ9qzZ0++QJf3Hp75XXu+bYoioDo3Z2dn5/vfVEhICHa7vdD/vRdlnwt53pKW97pn1xUaGlrkmp577jnmzp3LL7/84tZp9t///rfb87Zt25b27dszb948evTo4YHqi6+g9yCvI2Nhx3v11Ve7Bba+ffvSqlUrXnjhBSZMmHDBz1vSCqoJiv7eHjlyhJkzZ/L+++/ne+yXX35xPfdVV11FeHg4Y8aM4b777nMLWt6sKL/7nvh8eKPdu3czcOBAbrrpJm666SbX+saNG/PTTz+57vfp04djx47x1FNPsWjRIgsqvXDTpk1zvW89evQgPj6eIUOG8NBDD1GnTh3X+3d2J2lff2/HjRtH1apV6d27t9v64cOHu/0eX3311dSrV493332X559/vpSrzO/YsWNcd911dOrUyS2gnq0of2s99fc4oFp8YmJiOH78uNu65ORknE4nFSpUuOB9LuR5S1relVtn13Xs2LEi1TR27FjeeustZs2a5XYKD/J/WbRr146oqCjWr19/kVVfuILeg2PHjrkeK8jZxxEcHMwVV1zhdhwX8rwlraCaoOjv7RdffEFISEiBV0Kc/W/So0cPMjMz+fPPPy+84FJWlN/9i/18eKO9e/fSvXt3OnbsyP/93/+5PVZQUO7Ro4eln9kLVdDvqGEYbNy4ETj93iYlJblt58vv7alTp5g0aRLDhg3Ld+Xm2f8ekZGRdOjQwSve2+PHj3PVVVdRoUIFZsyYQXBw4W0t5/pbe67v2rO3KYqACj5t2rRhx44dbh+IvKbe1q1bX/A+F/K8Ja1hw4aEh4ezatUq1zrDMFi9evV5a3rttdd46aWXXJfTnk96ejppaWmEh4dfdN0Xqk2bNm7HCuZ7UKtWLcqXL1/k5zl48KDbcXjqeT2pTZs2pKenu13mumnTJk6ePFmk37dx48YxePDgIvU5OHjwIICl721xxcTEULNmzQLft7x/n6Js40sSEhLo1q0bLVq0YMqUKef8gslz9u+6rzr7dzTv9O2Z721SUhLbt2/3yfcWYPr06Rw/fpy77rqrSNt7w3ublJTEVVddRVhYGHPmzCmw38+Z2rRpwx9//EFaWpprXd73aKtWrYq8TZEUuRu0H0hNTTViY2ONxx57zDAMw8jKyjIuv/xy4/LLL3dtc+LECaNjx47GrFmzirxPUbaxwvDhw40GDRoYx44dMwzDvCIgODjY2LZtm2ubJ5980rjvvvtc99944w0jLCzMmD9/foHPeeTIEePDDz80cnJyDMMwr4QZNmyYERYWZullz4sXLzZsNpvxww8/GIZhGLt27TIqVqzodtnnggULjI4dOxqHDx82DMMwPvzwQyMxMdH1+Pfff28EBQUZH3zwQbGet7Tl5OQYDRo0MG6++WbD6XQaTqfTuPHGG40GDRq43hfDMIxu3boZ48ePd9t36dKlBmD89ttv+Z534cKFxrx581z39+/fb7Rq1cpo165diR1LcZzrqq4333zTGDRokOv+iy++aFSqVMnYvXu3YRiGMXPmTMNmsxm//vprsbaxUmFXdW3dutXo2LGjsX79esMwDGPfvn1G3bp1jeuvv944depUgc81YcIEY/v27a77y5cvNyIiIlx/s6x2rqu6hgwZYrzyyiuGYRjGhg0bjClTprgeS0pKMnr27GlUr17dbd/u3bsbXbt2dV3p8/jjjxsxMTGuq8isVthVXV9++aXRpUuXfOuvuuoqo2fPngU+1xtvvOF2XB9//LEBGDNnzvRcwcWUnJxstG3b1ujSpUuBV3gZhmEcO3bM6NixozF37lzDMMyhQ6Kjo41nnnnGMAzzCsdLLrnE6NWrl2ufomxTFAEVfAzDMH755RcjLi7OqF69ulG+fHmjVatWxp49e1yPJyUlGYDbF8b59inqNqUtNTXV6NWrlxEWFmbUrVvXiIiIML788ku3bfr16+cKaDt27DAAo2LFikbHjh3dbr/88othGGbQefTRR42YmBijefPmRnR0tNG0aVNj8eLFpX14+eSNj9GgQQPD4XAYQ4cOdfsiyPvjmpCQYBiGYUybNs2oW7euUb9+faNWrVpGRESE8corr7iNWVSU57XChg0bjHr16hmVK1c2KlWqZNSrV8/YsGGD2zbh4eH5AtqwYcMKvfR1z549Rt++fY2KFSsaTZs2NRwOh3Httde6/r2sMnfuXKNjx45G27ZtDcBo1KiR0bFjR+Pf//63a5sHHnjAqFu3ruv+qVOnjKFDhxoOh8OoX7++UbZs2XxjfRRlGyt07drV6NixoxEdHW1UqFDB6Nixo9G7d2/X43mXZucFtJtvvtkAjDZt2rh9Zu+44w7XPr/88ovRokULo2bNmq5jffTRRwsd06u0vPbaa0bHjh2N+vXrG4DRoUMHo2PHjsayZctc2zRt2tS46667DMMwvyxvu+0219+f8PBw47LLLss3jlhCQoLRpk0bIzo62oiPjzcqV65sLFiwoFSP7WybNm1yvTd2u92oXbu20bFjR+Mf//iHa5s33njDCAoKctsvb/iKwoaZ+M9//mNUrVrVaNy4sVG1alWjYsWKxueff16ix3I+TzzxhAEYzZo1c/udPPP3ODEx0QCMr776yrVu7ty5RqVKlYwaNWoYUVFRRvv27Y39+/e7PXdRtjmfgJydPScnh61bt+JwOGjQoIHbY7m5uaxatYq6deu6jd57rn2Ks40V9u7dy/Hjx2nQoAFhYWFuj23bto2cnByaNGlCZmYm69atK/A5GjZs6HZqJzMzk7/++ovY2FiqVKlSkuUXS2pqKjt27KBKlSrExcW5PXb8+HG2bdtGmzZtXB0fDcNg165dOJ1OatWqVegpgnM9r1WcTid//PEHYF6NZbe7n7letWoVVatWdbsKYv369URGRlK7du1CnzcpKYl9+/ZRs2ZNj1yCe7GOHTvG9u3b862vVq0a8fHxgNmpNzk5OV9zd2JiIgcPHqRu3bqFHktRtilNK1euxOl0uq0rU6aM6xROeno6GzdupGnTpkRERLB9+3ZXP4czlStXzu2yXzBPiaWnp1O7du1iXzZfEnbt2sWhQ4fyrW/cuLHrUvcNGzZQrlw56tSp43o8LS2NnTt3Uq1atXP27di+fTsZGRk0btyYkJAQzx9AMaSlpbFp06Z862NjY6lXrx5g/i7u3bvXbYTjvN//tm3bFnoMTqeT7du343A4qFGjRr6/BaVtz549BV71eubvcXZ2NmvWrKF+/fpu72F2djZbt24lLCzM9e9ytqJscy4BGXxEREQkMAVU52YREREJbAo+IiIiEjAUfERERCRgKPiIiIhIwFDwERERkYCh4CMiIiIBQ8FHREREAoaCj4iIiAQMBR8REREJGAo+IuLTsrOzmTJlCtu2bXNbv3DhQhYtWmRNUSLitRR8RMSnhYSEsHTpUnr37s2JEycA+PXXX+nZsyc5OTkWVyci3kZzdYmIz8vMzKR9+/a0bduWd999l5YtW3LDDTfw5ptvWl2aiHgZBR8R8QsbN26kQ4cO1K9fn6CgIFasWEGZMmWsLktEvIxOdYmIX2jevDk33ngjGzdu5PXXX1foEZECqcVHRPzC6tWr6dy5M/Xq1aNixYosXLgQu13/txMRd/qrICI+Lz09naFDh3LPPfcwf/58tmzZwmuvvWZ1WSLihdTiIyI+b/jw4fz666/8/vvvhIaGMmPGDG644QaWLVtG27ZtrS5PRLyIWnxExKft3r2bEydOMHnyZEJDQwHo168fL774IjNnzrS4OhHxNmrxERERkYChFh8REREJGAo+IiIiEjAUfERERCRgKPiIiIhIwFDwERERkYCh4CMiIiIBQ8FHREREAoaCj4iIiAQMBR8REREJGAo+IiIiEjAUfERERCRgKPiIiIhIwPh/23JSguoESZIAAAAASUVORK5CYII=",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 3d) SV Regressor\n",
|
|
"from sklearn.svm import SVR\n",
|
|
"\n",
|
|
"svr = SVR()\n",
|
|
"svr.fit(X.reshape(-1,1),T)\n",
|
|
"\n",
|
|
"plt.plot(X, T, '.')\n",
|
|
"plt.xlabel('x')\n",
|
|
"plt.ylabel('t')\n",
|
|
"plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "9ffc4d13",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 3e)\n",
|
|
"svr_iris = SVR()\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "nnw (3.14.x)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.14.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|