dataSet = x
for i in range(len(dataSet)):
X = dataSet[i][1]
Y = dataSet[i][2]
Z = dataSet[i][3]
if y[i] == 1:
p = ax.scatter(X, Y, Z, c='c', marker='^')
else:
p2 = ax.scatter(X, Y, Z, c='r', marker='o')
ax.legend([p, p2], ['Label 1 data', 'Label -1 data'])
xs, ys = np.meshgrid(np.arange(-0.2, 1.2, 0.02),
np.arange(-0.2, 1.2, 0.02))
zs = -(w[0] + w[1] * xs + w[2] * ys) / w[3]
ax.plot_surface(xs, ys, zs, color='b', alpha=0.3)
plt.show()
if __name__ == '__main__':
X, Y = inputData("classification.txt")
X = np.c_[np.ones(len(X)), np.array(X)]
pla = Perceptron()
pla.n_iter = 5000
print(pla.get_params())
pla = pla.fit(X, Y)
accuracy = pla.score(X, Y)
W = pla.coef_
print('Accuracy =', accuracy)
print('Weights =', W)
display(X, Y, W[0])
plt.ylabel('Error number')
plt.show()
# Waiting to implement
pass
if __name__ == '__main__':
"""
PLA
"""
X, Y = getInputData("classification.txt")
X = np.c_[np.ones(len(X)), np.array(
X
)] # Coordinates vector of points, which is added '1' at first column.
pla = Perceptron()
pla.n_iter = 200
info = pla.get_params()
print(info)
pla = pla.fit(X, Y)
score = pla.score(X, Y)
W = pla.coef_
print('score =', score)
print('W=', W)
plot(X, Y, W[0])
"""
pocket PLA
"""
iterList = []
numList = []
best_score = 0
W = None
"""
Created on Wed Apr 19 11:22:19 2017
@author: 3200183
"""
print(__doc__)
from sklearn import clone
from sklearn.linear_model import Perceptron
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import matplotlib.pyplot as plt
from arftools import *
model = Perceptron()
model.n_iter = 1000
#model = DecisionTreeClassifier()
#model.max_depth=3
(X,y) = gen_arti(centerx=1, centery=1, sigma=0.1, nbex=1000, data_type=1, epsilon=0.02)
d= np.ones(len(X))/len(X)
print len(d)
print len(y)
res = []
eps = 0.1
step=0
while eps <0.5 and eps>0 and step<100:
print(step)
