Add TP3 exo 1 first question implementation.

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/TP3/exo1/tp3_exo1.py b/TP3/exo1/tp3_exo1.py
new file mode 100755 (executable)
index 0000000..dfd04d8
--- /dev/null
+++ b/TP3/exo1/tp3_exo1.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+
+# -*- coding: utf-8 -*-
+import numpy as np
+from numpy.random import rand
+import pylab as pl
+
+
+def generateData(n):
+    """
+    Generates a 2D linearly separable dataset with 2n samples.
+    The third element of the sample is the label
+    """
+    linear_offset = 0.6
+    xb = (rand(n) * 2 - 1) / 2 - linear_offset
+    yb = (rand(n) * 2 - 1) / 2 + linear_offset
+    xr = (rand(n) * 2 - 1) / 2 + linear_offset
+    yr = (rand(n) * 2 - 1) / 2 - linear_offset
+    inputs = []
+    for i in range(n):
+        inputs.append([xb[i], yb[i], -1])
+        inputs.append([xr[i], yr[i], 1])
+    return inputs
+
+
+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
+    yb = (rand(n) * 2 - 1) / 2
+    xr = (rand(n) * 2 - 1) / 2 + 1.5
+    yr = (rand(n) * 2 - 1) / 2 - 0.5
+    inputs = []
+    for i in range(n):
+        inputs.append([xb[i], yb[i], -1])
+        inputs.append([xr[i], yr[i], 1])
+    return inputs
+
+
+training_set_size = 100
+training_set = generateData(training_set_size)
+data = np.array(training_set)
+X = data[:, 0:2]
+Y = data[:, -1]
+
+
+def perceptron_nobias(X, Y):
+    w = np.zeros([len(X[0])])
+    # Go in the loop at least one time
+    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:
+                classification_error = classification_error + 1
+                w = w + Y[i] * X[i]
+    return w
+
+
+def complete(sample):
+    sample = np.expand_dims(sample, axis=0)
+    return sample
+
+
+w = perceptron_nobias(X, Y)
+pl.plot([-1, 1], [w[0] / w[1], -w[0] / w[1]])
+pl.scatter(X[:, 0], X[:, 1], c=Y, s=training_set_size)
+pl.show()