TP3/exo1/tp3_exo1.py

   1 #!/usr/bin/env python3
   2
   3 # -*- coding: utf-8 -*-
   4 import numpy as np
   5 from numpy.random import rand
   6 import pylab as pl
   7
   8
   9 def generateData(n):
  10     """
  11     Generates a 2D linearly separable dataset with 2n samples.
  12     The third element of the sample is the label
  13     """
  14     linear_offset = 0.6
  15     xb = (rand(n) * 2 - 1) / 2 - linear_offset
  16     yb = (rand(n) * 2 - 1) / 2 + linear_offset
  17     xr = (rand(n) * 2 - 1) / 2 + linear_offset
  18     yr = (rand(n) * 2 - 1) / 2 - linear_offset
  19     inputs = []
  20     for i in range(n):
  21         inputs.append([xb[i], yb[i], -1])
  22         inputs.append([xr[i], yr[i], 1])
  23     return inputs
  24
  25
  26 def generateData2(n):
  27     """
  28     Generates a 2D linearly separable dataset with 2n samples.
  29     The third element of the sample is the label
  30     """
  31     xb = (rand(n) * 2 - 1) / 2 - 0.5
  32     yb = (rand(n) * 2 - 1) / 2
  33     xr = (rand(n) * 2 - 1) / 2 + 1.5
  34     yr = (rand(n) * 2 - 1) / 2 - 0.5
  35     inputs = []
  36     for i in range(n):
  37         inputs.append([xb[i], yb[i], -1])
  38         inputs.append([xr[i], yr[i], 1])
  39     return inputs
  40
  41
  42 training_set_size = 100
  43 training_set = generateData(training_set_size)
  44 data = np.array(training_set)
  45 X = data[:, 0:2]
  46 Y = data[:, -1]
  47
  48
  49 def perceptron_nobias(X, Y):
  50     w = np.zeros([len(X[0])])
  51     # Go in the loop at least one time
  52     classification_error = 1
  53     while not classification_error == 0:
  54         classification_error = 0
  55         for i in range(X.shape[0]):
  56             if Y[i] * np.dot(w, X[i]) <= 0:
  57                 classification_error = classification_error + 1
  58                 w = w + Y[i] * X[i]
  59     return w
  60
  61
  62 def complete(sample):
  63     sample = np.expand_dims(sample, axis=0)
  64     return sample
  65
  66
  67 w = perceptron_nobias(X, Y)
  68 pl.plot([-1, 1], [w[0] / w[1], -w[0] / w[1]])
  69 pl.scatter(X[:, 0], X[:, 1], c=Y, s=training_set_size)
  70 pl.show()