In [7]:

    

from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split


    

In [3]:

    

iris = datasets.load_iris()
print(iris.data.shape)


    

    




(150, 4)

In [4]:

    

X = iris.data[:, [2, 3]]
y = iris.target


    

In [6]:

    

print('Class labels:', np.unique(y))


    

    




Class labels: [0 1 2]

In [8]:

    

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)


    

In [15]:

    

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)


    

In [17]:

    

from sklearn.linear_model import Perceptron
ppn = Perceptron(max_iter=40, eta0=0.1, random_state=0, shuffle=True)
ppn.fit(X_train_std, y_train)


    

    
Out[17]:





Perceptron(alpha=0.0001, class_weight=None, eta0=0.1, fit_intercept=True,
      max_iter=40, n_iter=None, n_jobs=1, penalty=None, random_state=0,
      shuffle=True, tol=None, verbose=0, warm_start=False)

In [18]:

    

y_pred = ppn.predict(X_test_std)


    

In [19]:

    

print('Misclassified samples: %d' % (y_test != y_pred).sum())


    

    




Misclassified samples: 4

In [20]:

    

from sklearn.metrics import accuracy_score
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))


    

    




Accuracy: 0.91

In [21]:

    

from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt


    

In [108]:

    

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    
    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())
    
    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)
    
    if test_idx:
        X_test, y_test = X[test_idx, :], y[test_idx]
        plt.scatter(X_test[:, 0], X_test[:, 1], facecolors='none', edgecolors='k',
                    alpha=1.0, linewidths=1, marker='o',
                    s=55, label='test set')


    

In [109]:

    

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.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()


    

In [145]:

    

import matplotlib.pyplot as plt
import numpy as np

def sigmoid(z):
    return 1.0 / (1.0 + np.exp(-z))

z = np.arange(-7, 7, 0.1)
phi_z = sigmoid(z)
plt.plot(z, phi_z)
plt.axvline(0.0, color='k')
plt.ylim(-0.1, 1.1)
plt.xlabel('z')
plt.ylabel('$\phi(z)$')
plt.yticks([0.0, 0.5, 1.0])
ax = plt.gca()
ax.yaxis.grid(True)
plt.show()


    

In [146]:

    

from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(C=1000.0, random_state=0)
lr.fit(X_train_std, y_train)


    

    
Out[146]:





LogisticRegression(C=1000.0, class_weight=None, dual=False,
          fit_intercept=True, intercept_scaling=1, max_iter=100,
          multi_class='ovr', n_jobs=1, penalty='l2', random_state=0,
          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)

In [149]:

    

plot_decision_regions(X_combined_std, y_combined, classifier=lr, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()


    

In [156]:

    

X_test_std[0, :].reshape(1, -1)


    

    
Out[156]:





array([[0.70793846, 1.50872803]])

In [157]:

    

lr.predict_proba(X_test_std[0, :].reshape(1, -1))


    

    
Out[157]:





array([[2.05743774e-11, 6.31620264e-02, 9.36837974e-01]])

In [ ]: