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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C1 - Neural Networks and Deep Learning/Week 3/Planar data classification with one hidden layer/planar_utils.py
Views: 4798
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import matplotlib.pyplot as plt

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import numpy as np

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import sklearn

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import sklearn.datasets

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import sklearn.linear_model

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def plot_decision_boundary(model, X, y):

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    # Set min and max values and give it some padding

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    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1

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    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1

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    h = 0.01

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    # Generate a grid of points with distance h between them

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    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

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    # Predict the function value for the whole grid

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    Z = model(np.c_[xx.ravel(), yy.ravel()])

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    Z = Z.reshape(xx.shape)

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    # Plot the contour and training examples

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    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)

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    plt.ylabel('x2')

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    plt.xlabel('x1')

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    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)

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def sigmoid(x):

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    """

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    Compute the sigmoid of x

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    Arguments:

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    x -- A scalar or numpy array of any size.

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    Return:

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    s -- sigmoid(x)

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    """

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    s = 1/(1+np.exp(-x))

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    return s

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def load_planar_dataset():

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    np.random.seed(1)

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    m = 400 # number of examples

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    N = int(m/2) # number of points per class

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    D = 2 # dimensionality

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    X = np.zeros((m,D)) # data matrix where each row is a single example

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    Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)

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    a = 4 # maximum ray of the flower

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    for j in range(2):

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        ix = range(N*j,N*(j+1))

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        t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta

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        r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius

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        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]

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        Y[ix] = j

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    X = X.T

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    Y = Y.T

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    return X, Y

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def load_extra_datasets():  

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    N = 200

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    noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)

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    noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)

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    blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)

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    gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)

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    no_structure = np.random.rand(N, 2), np.random.rand(N, 2)

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    return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

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