April 2018
Beginner
552 pages
13h 58m
English
import numpy as npfrom sklearn import linear_modelimport matplotlib.pyplot as plta = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])b = np.array([1, 1, 1, 2, 2, 2])
classification = linear_model.LogisticRegression(solver='liblinear', C=100)classification.fit(a, b)
def plot_classification(classification, a , b): a_min, a_max = min(a[:, 0]) - 1.0, max(a[:, 0]) + 1.0 b_min, b_max = min(a[:, 1]) - 1.0, max(a[:, 1]) + 1.0 step_size = 0.01 a_values, b_values = np.meshgrid(np.arange(a_min, a_max, step_size), np.arange(b_min, b_max, step_size)) mesh_output1 = classification.predict(np.c_[a_values.ravel(), ...