y = elems.pop()
y = float(y.rstrip())
xs = [float(elem) for elem in elems]
if int(y) == 0:
class1.append(xs)
else:
class2.append(xs)
class1 = np.array(class1)
class2 = np.array(class2)
return class1, class2
class1, class2 = read_data()
# plot the raw data
figure(0)
pl.plot(class1[:, 0], class1[:, 1], "xr")
pl.plot(class2[:, 0], class2[:, 1], "ob")
figure(1)
m = FisherClassifier(class1, class2)
m.fit()
pl.plot(np.dot(class1, m.w), [0] * class1.shape[0], "bo")
pl.plot(np.dot(class2, m.w), [0] * class2.shape[0], "r-")
pl.show()
f=open('..\\dataset\\bezdekIris.data', 'r')
lines = [line.strip() for line in f.readlines()]
random.shuffle(lines)
f.close()
lines = [line.split(',') for line in lines if line]
class1 = np.array([line[:4] for line in lines[0:N] if line[-1] == 'Iris-setosa'], dtype=np.float)
class2 = np.array([line[:4] for line in lines[0:N] if line[-1] != 'Iris-setosa'], dtype=np.float)
class1_tests = np.array([line[:4] for line in lines[N:] if line[-1] == 'Iris-setosa'], dtype=np.float)
class2_tests = np.array([line[:4] for line in lines[N:] if line[-1] != 'Iris-setosa'], dtype=np.float)
return class1, class2, class1_tests, class2_tests
if __name__ == '__main__':
c1, c2, c1test, c2test = read_data()
m = FisherClassifier(c1, c2)
m.fit()
pl.plot(np.dot(c1, m.w), [0]*c1.shape[0], 'bo')
pl.plot(np.dot(c2, m.w), [0]*c2.shape[0], 'r-')
pl.show()
error = 0
for i in range(len(c1test)):
y = m.predict(c1test[i])
if y > 0:
error += 1
for i in range(len(c2test)):
y = m.predict(c2test[i])
if y < 0:
y = elems.pop()
y = float(y.rstrip())
xs = [float(elem) for elem in elems]
if int(y) == 0:
class1.append(xs)
else:
class2.append(xs)
class1 = np.array(class1)
class2 = np.array(class2)
return class1, class2
class1, class2 = read_data()
# plot the raw data
figure(0)
pl.plot(class1[:, 0], class1[:, 1], 'xr')
pl.plot(class2[:, 0], class2[:, 1], 'ob')
figure(1)
m = FisherClassifier(class1, class2)
m.fit()
pl.plot(np.dot(class1, m.w), [0] * class1.shape[0], 'bo')
pl.plot(np.dot(class2, m.w), [0] * class2.shape[0], 'r-')
pl.show()
