Created
October 6, 2017 10:02
-
-
Save de-sh/f0013f3c51263e65b57549eda4e60258 to your computer and use it in GitHub Desktop.
NN
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
from sklearn import datasets, linear_model | |
import matplotlib.pyplot as plt | |
def generate_data(): | |
np.random.seed(0) | |
X, y = datasets.make_moons(200, noise=0.20) | |
return X, y | |
def visualize(X, y, clf): | |
# plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral) | |
# plt.show() | |
plot_decision_boundary(lambda x: clf.predict(x), X, y) | |
plt.title("Logistic Regression") | |
def plot_decision_boundary(pred_func, X, y): | |
# Set min and max values and give it some padding | |
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 | |
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 | |
h = 0.01 | |
# Generate a grid of points with distance h between them | |
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) | |
# Predict the function value for the whole gid | |
Z = pred_func(np.c_[xx.ravel(), yy.ravel()]) | |
Z = Z.reshape(xx.shape) | |
# Plot the contour and training examples | |
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) | |
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral) | |
plt.show() | |
def classify(X, y): | |
clf = linear_model.LogisticRegressionCV() | |
clf.fit(X, y) | |
return clf | |
def main(): | |
X, y = generate_data() | |
# visualize(X, y) | |
clf = classify(X, y) | |
visualize(X, y, clf) | |
if __name__ == "__main__": | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment