update

Author: bob7783

logistic_regression_class/l1_regularization.py

@@ -9,6 +9,7 @@
 
 
 import numpy as np
+from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 
 def sigmoid(z):
@@ -19,13 +20,23 @@ def sigmoid(z):
 
 # uniformly distributed numbers between -5, +5
 X = (np.random.random((N, D)) - 0.5)*10
+# X = (np.random.randn(N, D) - 0.5)*10
 
 # true weights - only the first 3 dimensions of X affect Y
 true_w = np.array([1, 0.5, -0.5] + [0]*(D - 3))
 
 # generate Y - add noise with variance 0.5
 Y = np.round(sigmoid(X.dot(true_w) + np.random.randn(N)*0.5))
 
+
+
+
+# let's plot the data to see what it looks like
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(X[:,0], X[:,1], X[:,2], c=Y)
+plt.show()
+
 # perform gradient descent to find w
 costs = [] # keep track of squared error cost
 w = np.random.randn(D) / np.sqrt(D) # randomly initialize w

logistic_regression_class/logistic3.py

@@ -65,7 +65,6 @@ def cross_entropy(T, Y):
         print(cross_entropy(T, Y))
 
     # gradient descent weight udpate
-    # w += learning_rate * np.dot((T - Y).T, Xb) # old
     w += learning_rate * Xb.T.dot(T - Y)
 
     # recalculate Y

logistic_regression_class/logistic4.py

@@ -64,7 +64,6 @@ def cross_entropy(T, Y):
         print(cross_entropy(T, Y))
 
     # gradient descent weight udpate with regularization
-    # w += learning_rate * ( np.dot((T - Y).T, Xb) - 0.1*w ) # old
     w += learning_rate * ( Xb.T.dot(T - Y) - 0.1*w )
 
     # recalculate Y

logistic_regression_class/logistic_donut.py

@@ -73,7 +73,6 @@ def cross_entropy(T, Y):
         print(e)
 
     # gradient descent weight udpate with regularization
-    # w += learning_rate * ( np.dot((T - Y).T, Xb) - 0.01*w ) # old
     w += learning_rate * ( Xb.T.dot(T - Y) - 0.1*w )
 
     # recalculate Y