X-Git-Url: https://git.piment-noir.org/?a=blobdiff_plain;f=TP3%2Fexo1%2Ftp3_exo1.py;h=02416ba46171c462dde7934537888c37661bdd3f;hb=f08c4a957aa85ba34302bfa77dfe92fdd5e9668c;hp=dfd04d831840fa1024b5a45c35073325826d4ffa;hpb=6f4ffbd78d75ea191f5d7708c1fe3a6f5ae734b5;p=TP_AA.git

diff --git a/TP3/exo1/tp3_exo1.py b/TP3/exo1/tp3_exo1.py
index dfd04d8..02416ba 100755
--- 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
@@ -39,8 +39,8 @@ def generateData2(n):
     return inputs
 
 
-training_set_size = 100
-training_set = generateData(training_set_size)
+training_set_size = 150
+training_set = generateData2(training_set_size)
 data = np.array(training_set)
 X = data[:, 0:2]
 Y = data[:, -1]
@@ -54,17 +54,29 @@ def perceptron_nobias(X, Y):
         classification_error = 0
         for i in range(X.shape[0]):
             if Y[i] * np.dot(w, X[i]) <= 0:
-                classification_error = classification_error + 1
+                classification_error += 1
                 w = w + Y[i] * X[i]
     return w
 
 
 def complete(sample):
-    sample = np.expand_dims(sample, axis=0)
-    return sample
+    new_sample = np.insert(sample, len(sample[0]), [1], axis=1)
+    return np.array(new_sample)
 
 
+X = complete(X)
 w = perceptron_nobias(X, Y)
-pl.plot([-1, 1], [w[0] / w[1], -w[0] / w[1]])
+# 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
+# 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()