TP4/exo1/tp4_exo1.py

   1 #!/usr/bin/env python3
   2
   3 # -*- coding: utf-8 -*-
   4 import numpy as np
   5 import pylab as pl
   6 from mpl_toolkits.mplot3d import Axes3D
   7
   8
   9 data = np.loadtxt("dataRegLin2D.txt")
  10 X = data[:, 0:2]
  11 Y = data[:, -1]
  12
  13
  14 def complete(sample):
  15     if sample.ndim > 1:
  16         ones = np.ones((sample.shape[0], 1))
  17         new_sample = np.append(sample, ones, axis=-1)
  18     else:
  19         new_sample = []
  20         for s in sample:
  21             s = [s, 1]
  22             new_sample.append(s)
  23     return np.array(new_sample)
  24
  25
  26 def train_regression(X, Y):
  27     X = complete(X)
  28     return np.dot(np.dot(np.linalg.inv(np.dot(np.transpose(X), X)), np.transpose(X)), Y)
  29
  30
  31 def predict(x, w):
  32     return np.dot(w[:len(w) - 1], x) + w[-1]
  33
  34
  35 def error(X, Y, w, idx):
  36     err = 0.0
  37     for i in range(len(X)):
  38         y = predict(X[i, :idx], w)
  39         err += (y - Y[i])**2
  40     err /= len(X)
  41     return err
  42
  43
  44 fig = pl.figure()
  45 ax = fig.add_subplot(131, projection='3d')
  46 ax.scatter(X[:, 0], X[:, 1], Y)
  47 w1 = train_regression(X, Y)
  48 print(error(X, Y, w1, 2))
  49
  50 ax = fig.add_subplot(132)
  51 ax.scatter(X[:, 0], Y[:])
  52 w2 = train_regression(X[:, 0], Y)
  53 print(error(X[:, 0].reshape((len(X), 1)), Y, w2, 1))
  54
  55 ax = fig.add_subplot(133)
  56 ax.scatter(X[:, 1], Y[:])
  57 w3 = train_regression(X[:, 1], Y)
  58 print(error(X[:, 1].reshape((len(X), 1)), Y, w3, 1))
  59
  60 pl.show()