πως να κανω subltot την εικονα σε αυτο των κωδικα?
# -*- coding: utf-8 -*-
"""
"""
from sklearn import datasets
import numpy as np
iris = datasets.load_iris() # fortosi dataset
print 'ena mikro deigma tou dataset'
print 'attribute values = ' , iris.data[0:5]
print 'class values = ' , iris.target[0:5]
print 'onomasia twn atributes'
print iris.feature_names
print 'onomasia twn katigoriwn'
print iris.target_names
print ' '
print 'diastaseis tou dataset = ' , iris.data.shape
print type(iris), type(iris.data), type(iris.target)
# xrhsh mono ton xaraktiristikon 3 kai 4 (2,3 gia python)
X = iris.data[:, [2, 3]]
y = iris.target
print ' '
print('Class labels:', np.unique(y))
# xorismos dedomenon se 70% train kai 30% test
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size = 0.1, random_state = 0)
# kanonikopoihsh ton dedomenon
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
from sklearn.linear_model import Perceptron
ppn = Perceptron(n_iter=100, eta0=0.1, random_state=0)
ppn.fit(X_train_std, y_train)
y_test.shape
y_pred = ppn.predict(X_test_std)
print('Misclassified samples: %d' % (y_test != y_pred).sum())
from sklearn.metrics import accuracy_score
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
#matplotlib inline
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
# plot all samples
X_test, y_test = X[test_idx, :], y[test_idx]
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
alpha=0.8, c=cmap(idx),
marker=markers[idx], label=cl)
# highlight test samples
if test_idx:
X_test, y_test = X[test_idx, :], y[test_idx]
plt.scatter(X_test[:, 0], X_test[:, 1], c='',
alpha=1.0, linewidth=1, marker='o',
s=55, label='test set')
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std, y=y_combined,
classifier=ppn, test_idx=range(105,150))
#plt.figure(num=None, figsize=(10,8 ), dpi=300, facecolor='w', edgecolor='k')
plt.xlabel('petal length [standardized]' , fontsize = 20)
plt.ylabel('petal width [standardized]', fontsize = 20)
plt.legend(loc='upper left' , fontsize = 20)
plt.title('100 iterations eta = 0.1', fontsize = 24)
plt.tight_layout()
# plt.savefig('./figures/iris_perceptron_scikit.png', dpi=300)
plt.show()
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