übung 3

Übung 3/SLN_Vorlage.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# %%
+import numpy as np
+import nnwplot
+import matplotlib.pyplot as plt
+
+# %% ErrorRate
+def ErrorRate(Y,T):
+    if Y.ndim==1 or Y.shape[0]==1:
+        errors=Y!=T
+        return errors.sum()/Y.size
+    else: # für mehrere Ausgaben in one-hot Kodierung:
+        errors=Y.argmax(0)!=T.argmax(0)
+        return errors.sum()/Y.shape[1]
+
+# %%
+class SLN:
+    def __init__(...): # Konstruktor
+        np.random.seed(42)
+        self._b=np.zeros((AAA,1))
+        self._W=np.random.randn(BBB,CCC)/np.sqrt(DDD)
+
+    # def DeltaTrain(self, X, T, eta, maxIter, maxErrorRate):
+    #     N=X.shape[1]    # Anzahl Trainingsdaten
+    #     plt.ion() # interactive mode on
+    #     for it in range(maxIter):
+    #         Y = ... # nur ein Aufruf
+    #         err = ErrorRate(..., ...)
+    #         if (it%20) == 0:
+    #             print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+    #             nnwplot.plotTwoFeatures(...)
+    #             plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+    #         if err <= maxErrorRate:
+    #             break
+    #         # update _W
+    #         ...
+    #         # update _b 
+    #         ...
+    #     print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+    #     nnwplot.plotTwoFeatures(...)
+    #     plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+    #
+    #     input("Press Enter to continue...") # wenn gewünscht ...

Übung 3/data/iris.csv

@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,0
+4.9,3.0,1.4,0.2,0
+4.7,3.2,1.3,0.2,0
+4.6,3.1,1.5,0.2,0
+5.0,3.6,1.4,0.2,0
+5.4,3.9,1.7,0.4,0
+4.6,3.4,1.4,0.3,0
+5.0,3.4,1.5,0.2,0
+4.4,2.9,1.4,0.2,0
+4.9,3.1,1.5,0.1,0
+5.4,3.7,1.5,0.2,0
+4.8,3.4,1.6,0.2,0
+4.8,3.0,1.4,0.1,0
+4.3,3.0,1.1,0.1,0
+5.8,4.0,1.2,0.2,0
+5.7,4.4,1.5,0.4,0
+5.4,3.9,1.3,0.4,0
+5.1,3.5,1.4,0.3,0
+5.7,3.8,1.7,0.3,0
+5.1,3.8,1.5,0.3,0
+5.4,3.4,1.7,0.2,0
+5.1,3.7,1.5,0.4,0
+4.6,3.6,1.0,0.2,0
+5.1,3.3,1.7,0.5,0
+4.8,3.4,1.9,0.2,0
+5.0,3.0,1.6,0.2,0
+5.0,3.4,1.6,0.4,0
+5.2,3.5,1.5,0.2,0
+5.2,3.4,1.4,0.2,0
+4.7,3.2,1.6,0.2,0
+4.8,3.1,1.6,0.2,0
+5.4,3.4,1.5,0.4,0
+5.2,4.1,1.5,0.1,0
+5.5,4.2,1.4,0.2,0
+4.9,3.1,1.5,0.1,0
+5.0,3.2,1.2,0.2,0
+5.5,3.5,1.3,0.2,0
+4.9,3.1,1.5,0.1,0
+4.4,3.0,1.3,0.2,0
+5.1,3.4,1.5,0.2,0
+5.0,3.5,1.3,0.3,0
+4.5,2.3,1.3,0.3,0
+4.4,3.2,1.3,0.2,0
+5.0,3.5,1.6,0.6,0
+5.1,3.8,1.9,0.4,0
+4.8,3.0,1.4,0.3,0
+5.1,3.8,1.6,0.2,0
+4.6,3.2,1.4,0.2,0
+5.3,3.7,1.5,0.2,0
+5.0,3.3,1.4,0.2,0
+7.0,3.2,4.7,1.4,1
+6.4,3.2,4.5,1.5,1
+6.9,3.1,4.9,1.5,1
+5.5,2.3,4.0,1.3,1
+6.5,2.8,4.6,1.5,1
+5.7,2.8,4.5,1.3,1
+6.3,3.3,4.7,1.6,1
+4.9,2.4,3.3,1.0,1
+6.6,2.9,4.6,1.3,1
+5.2,2.7,3.9,1.4,1
+5.0,2.0,3.5,1.0,1
+5.9,3.0,4.2,1.5,1
+6.0,2.2,4.0,1.0,1
+6.1,2.9,4.7,1.4,1
+5.6,2.9,3.6,1.3,1
+6.7,3.1,4.4,1.4,1
+5.6,3.0,4.5,1.5,1
+5.8,2.7,4.1,1.0,1
+6.2,2.2,4.5,1.5,1
+5.6,2.5,3.9,1.1,1
+5.9,3.2,4.8,1.8,1
+6.1,2.8,4.0,1.3,1
+6.3,2.5,4.9,1.5,1
+6.1,2.8,4.7,1.2,1
+6.4,2.9,4.3,1.3,1
+6.6,3.0,4.4,1.4,1
+6.8,2.8,4.8,1.4,1
+6.7,3.0,5.0,1.7,1
+6.0,2.9,4.5,1.5,1
+5.7,2.6,3.5,1.0,1
+5.5,2.4,3.8,1.1,1
+5.5,2.4,3.7,1.0,1
+5.8,2.7,3.9,1.2,1
+6.0,2.7,5.1,1.6,1
+5.4,3.0,4.5,1.5,1
+6.0,3.4,4.5,1.6,1
+6.7,3.1,4.7,1.5,1
+6.3,2.3,4.4,1.3,1
+5.6,3.0,4.1,1.3,1
+5.5,2.5,4.0,1.3,1
+5.5,2.6,4.4,1.2,1
+6.1,3.0,4.6,1.4,1
+5.8,2.6,4.0,1.2,1
+5.0,2.3,3.3,1.0,1
+5.6,2.7,4.2,1.3,1
+5.7,3.0,4.2,1.2,1
+5.7,2.9,4.2,1.3,1
+6.2,2.9,4.3,1.3,1
+5.1,2.5,3.0,1.1,1
+5.7,2.8,4.1,1.3,1
+6.3,3.3,6.0,2.5,2
+5.8,2.7,5.1,1.9,2
+7.1,3.0,5.9,2.1,2
+6.3,2.9,5.6,1.8,2
+6.5,3.0,5.8,2.2,2
+7.6,3.0,6.6,2.1,2
+4.9,2.5,4.5,1.7,2
+7.3,2.9,6.3,1.8,2
+6.7,2.5,5.8,1.8,2
+7.2,3.6,6.1,2.5,2
+6.5,3.2,5.1,2.0,2
+6.4,2.7,5.3,1.9,2
+6.8,3.0,5.5,2.1,2
+5.7,2.5,5.0,2.0,2
+5.8,2.8,5.1,2.4,2
+6.4,3.2,5.3,2.3,2
+6.5,3.0,5.5,1.8,2
+7.7,3.8,6.7,2.2,2
+7.7,2.6,6.9,2.3,2
+6.0,2.2,5.0,1.5,2
+6.9,3.2,5.7,2.3,2
+5.6,2.8,4.9,2.0,2
+7.7,2.8,6.7,2.0,2
+6.3,2.7,4.9,1.8,2
+6.7,3.3,5.7,2.1,2
+7.2,3.2,6.0,1.8,2
+6.2,2.8,4.8,1.8,2
+6.1,3.0,4.9,1.8,2
+6.4,2.8,5.6,2.1,2
+7.2,3.0,5.8,1.6,2
+7.4,2.8,6.1,1.9,2
+7.9,3.8,6.4,2.0,2
+6.4,2.8,5.6,2.2,2
+6.3,2.8,5.1,1.5,2
+6.1,2.6,5.6,1.4,2
+7.7,3.0,6.1,2.3,2
+6.3,3.4,5.6,2.4,2
+6.4,3.1,5.5,1.8,2
+6.0,3.0,4.8,1.8,2
+6.9,3.1,5.4,2.1,2
+6.7,3.1,5.6,2.4,2
+6.9,3.1,5.1,2.3,2
+5.8,2.7,5.1,1.9,2
+6.8,3.2,5.9,2.3,2
+6.7,3.3,5.7,2.5,2
+6.7,3.0,5.2,2.3,2
+6.3,2.5,5.0,1.9,2
+6.5,3.0,5.2,2.0,2
+6.2,3.4,5.4,2.3,2
+5.9,3.0,5.1,1.8,2
+

Übung 3/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()