TP3: print perceptron errors.

[TP_AA.git] / TP3 / exo1 / tp3_exo1.py

diff --git a/TP3/exo1/tp3_exo1.py b/TP3/exo1/tp3_exo1.py
index 73dd307de18e12ea18c25324c03142001c04c12d..57312f2cce03a9af40692e3df7210057dc9f5186 100755 (executable)
--- a/TP3/exo1/tp3_exo1.py
+++ b/TP3/exo1/tp3_exo1.py
@@ -28,7 +28,7 @@ def generateData2(n):
     Generates a 2D linearly separable dataset with 2n samples.
     The third element of the sample is the label
     """
-    xb = (rand(n) * 2 - 1) / 2 - 0.5
+    xb = (rand(n) * 2 - 1) / 2 + 0.5
     yb = (rand(n) * 2 - 1) / 2
     xr = (rand(n) * 2 - 1) / 2 + 1.5
     yr = (rand(n) * 2 - 1) / 2 - 0.5
@@ -52,10 +52,11 @@ def perceptron_nobias(X, Y):
     classification_error = 1
     while not classification_error == 0:
         classification_error = 0
-        for i in range(X.shape[0]):
-            if Y[i] * np.dot(w, X[i]) <= 0:
+        for x, y in zip(X, Y):
+            if y * np.dot(w, x) <= 0:
                 classification_error += 1
-                w = w + Y[i] * X[i]
+                w = w + y * x
+        print(classification_error)
     return w
 
 
@@ -68,10 +69,15 @@ X = complete(X)
 w = perceptron_nobias(X, Y)
 # w is orthogonal to the hyperplan
 # with generateData
+# plot arguments format is pl.plot([x1,x2],[y1,y2])
+# w[0]x + w[1]y = 0, so y = -w[0]x / w[1]
 # pl.plot([-1, 1], [w[0] / w[1], -w[0] / w[1]])
 # with generateData2 and complete
-# FIXME: the hyperplan equation is not correct
-pl.plot([0, -1 / w[1]], [w[0] / w[1] - 1 / w[1], -w[0] / w[1] - 1 / w[1]])
+# w[0]x + w[1]y + w[2] = 0, so y = -(w[0]x + w[2]) / w[1]
+x_start1 = -0.5
+x_start2 = 2.5
+pl.plot([x_start1, x_start2], [-(w[0] * x_start1 + w[2]) /
+                               w[1], -(w[0] * x_start2 + w[2]) / w[1]])
 pl.scatter(X[:, 0], X[:, 1], c=Y, s=training_set_size)
 pl.title(u"Perceptron - hyperplan")
 pl.show()