diff --git a/Aufgabenblätter/NNW_Aufgabenblatt2.pdf b/Aufgabenblätter/NNW_Aufgabenblatt2.pdf new file mode 100644 index 0000000..127c9d5 Binary files /dev/null and b/Aufgabenblätter/NNW_Aufgabenblatt2.pdf differ diff --git a/pyproject.toml b/pyproject.toml index 77a9750..b431a4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,7 @@ dependencies = [ "numpy>=2.5.3", "pandas>=3.0.6", "plotly>=7.1.0", + "scikit-learn>=1.9.1", ] [dependency-groups] diff --git a/supplement_files/nnwplot.py b/supplement_files/nnwplot.py new file mode 100644 index 0000000..8b715cd --- /dev/null +++ b/supplement_files/nnwplot.py @@ -0,0 +1,49 @@ +# based on: https://github.com/eakbas/tf-svm/blob/master/plot_boundary_on_data.py +import numpy as np +from matplotlib import pyplot as plt +from matplotlib import colors + + +def plotTwoFeatures(X,T,pred_func): + if X.ndim!=2: + raise ValueError('X be a matrix (2 dimensional array).') + tp=False + if X.shape[0]!=2 and X.shape[1]==2: + X=X.T + tp=True + if X.shape[0]!=2: + raise ValueError('X must contain exactly 2 features.') + + # determine canvas borders + mins = np.amin(X,1); + mins = mins - 0.1*np.abs(mins); + maxs = np.amax(X,1); + maxs = maxs + 0.1*maxs; + + ## generate dense grid + xs,ys = np.meshgrid(np.linspace(mins[0],maxs[0],300), + np.linspace(mins[1], maxs[1], 300)); + + + # evaluate model on the dense grid + try: + Z = pred_func(np.c_[xs.flatten(), ys.flatten()].T); + except: + Z = pred_func(np.c_[xs.flatten(), ys.flatten()]); + + if tp: + Z=Z.T + T=T.T + + if Z.ndim>1 and Z.shape[0]>1: # onehot? -> convert + Z=Z.argmax(0) + Z = Z.reshape(xs.shape) + + if T.ndim>1 and T.shape[0]>1: # onehot? -> convert + T=T.argmax(0) + + # Plot the contour and training examples + plt.contourf(xs, ys, Z, cmap=plt.cm.Spectral) + plt.scatter(X[0,:], X[1,:], c=T, s=50, + cmap=colors.ListedColormap(['orange', 'blue', 'green'])) + plt.show() diff --git a/uv.lock b/uv.lock index 9914440..c60d126 100644 --- a/uv.lock +++ b/uv.lock @@ -93,6 +93,15 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