def test_linear_regression():
"""
Test set "stolen" from scikit learn
"""
# this is our test set, it's just a straight line with some
# gaussian noise
xmin, xmax = -5, 5
n_samples = 100
X = np.array([[i] for i in np.linspace(xmin, xmax, n_samples)])
Y = np.array(2 + 0.5 * np.linspace(xmin, xmax, n_samples) \
+ np.random.randn(n_samples, 1).ravel())
beta, u = linear_regression(X, Y)
plt.scatter(X, Y, color='black')
plt.plot(X, np.dot(X, beta) + u, linewidth=1)
plt.show()
def plot(X, Y, XtA, title="ClassificationA.png"):
fig = plt.figure()
colors = ['#4EACC5', '#FF9C34', '#aaaaaa', '#4E9A06', '#00465F', "#7E2007"]
my_members = Y == 0
my_members.shape = (my_members.shape[0])
ax = fig.add_subplot(1, 1, 1)
ax.plot(X[my_members, 0], X[my_members, 1],
'w', markerfacecolor=colors[0], marker = '.')
my_members = Y == 1
my_members.shape = (my_members.shape[0])
ax.plot(X[my_members, 0], X[my_members, 1],
'w', markerfacecolor=colors[1], marker = '.')
beta, u = classification.LDA(X, Y)
YtcA = classification.logistic_regression_predict(XtA, beta, u)
x_beta = [[i] for i in np.linspace(X.min(), X.max(), 100)]
y_beta = (- u - beta[0] * np.linspace(X.min(), X.max(), 100)) * 1 / beta[1]
ax.plot(x_beta, y_beta, color=colors[3], linewidth=1)
beta, u = classification.logistic_regression(X, Y, verbose=False)
x_beta = [[i] for i in np.linspace(X.min(), X.max(), 100)]
y_beta = (- u - beta[0] * np.linspace(X.min(), X.max(), 100)) * 1 / beta[1]
ax.plot(x_beta, y_beta, color=colors[4], linewidth=1)
YtcA = classification.logistic_regression_predict(XtA, beta, u)
beta, u = classification.linear_regression(X, Y)
YtcA = classification.linear_regression_predict(XtA, beta, u)
x_beta = [[i] for i in np.linspace(X.min(), X.max(), 100)]
y_beta = (0.5 - u - beta[0] * np.linspace(X.min(), X.max(), 100)) * 1 / beta[1]
ax.plot(x_beta, y_beta, color=colors[5], linewidth=1)
labels = ('unknown', 'label 0', 'label 1', 'LDA model', 'logistic regression', 'linear regression')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
plt.show()
plt.savefig(title)
print "****************" print beta, u = classification.LDA(XA, YA) YtcA = classification.logistic_regression_predict(XtA, beta, u) erreur = classification.error(YtcA, YtA) print "Jeu de test A - Modèle LDA: erreur %s" % erreur beta, u = classification.logistic_regression(XA, YA, verbose=False) YtcA = classification.logistic_regression_predict(XtA, beta, u) erreur = classification.error(YtcA, YtA) print "Jeu de test A - Regression logisitique: erreur %s" % erreur beta, u = classification.linear_regression(XA, YA) YtcA = classification.linear_regression_predict(XtA, beta, u) erreur = classification.error(YtcA, YtA) print "Jeu de test A - Regression linéaire: erreur %s" % erreur # Jeu de données B print print print "Jeu de données B" print "****************" print beta, u = classification.LDA(XB, YB) YtcB = classification.logistic_regression_predict(XtB, beta, u) erreur = classification.error(YtcB, YtB)
import numpy as np
from mpl_toolkits.axes_grid.axislines import SubplotZero
import matplotlib.pyplot as plt
from classification import linear_regression
from utils import load_data
verbose = True
max_iter = 500
X, Y = load_data('classificationA.train')
beta, u = linear_regression(X, Y)
# Let's plot the result
fig = plt.figure(1)
colors = ['#4EACC5', '#FF9C34', '#4E9A06']
my_members = Y == 0
my_members.shape = (my_members.shape[0])
ax = SubplotZero(fig, 111)
fig.add_subplot(ax)
for direction in ["xzero", "yzero"]:
ax.axis[direction].set_axisline_style("-|>")
ax.axis[direction].set_visible(True)
for direction in ["left", "right", "bottom", "top"]:
ax.axis[direction].set_visible(False)
