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diff --git a/OLD/csci4511w/colab/HW5 - Neural Networks/planar_utils.py b/OLD/csci4511w/colab/HW5 - Neural Networks/planar_utils.py
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+++ b/OLD/csci4511w/colab/HW5 - Neural Networks/planar_utils.py
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+import matplotlib.pyplot as plt
+import numpy as np
+import sklearn
+import sklearn.datasets
+import sklearn.linear_model
+
+def plot_decision_boundary(model, X, y):
+    # Set min and max values and give it some padding
+    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
+    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
+    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 grid
+    Z = model(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.ylabel('x2')
+    plt.xlabel('x1')
+    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
+
+
+def sigmoid(x):
+    """
+    Compute the sigmoid of x
+
+    Arguments:
+    x -- A scalar or numpy array of any size.
+
+    Return:
+    s -- sigmoid(x)
+    """
+    s = 1/(1+np.exp(-x))
+    return s
+
+def load_planar_dataset():
+    np.random.seed(1)
+    m = 400 # number of examples
+    N = int(m/2) # number of points per class
+    D = 2 # dimensionality
+    X = np.zeros((m,D)) # data matrix where each row is a single example
+    Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)
+    a = 4 # maximum ray of the flower
+
+    for j in range(2):
+        ix = range(N*j,N*(j+1))
+        t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta
+        r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
+        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
+        Y[ix] = j
+
+    X = X.T
+    Y = Y.T
+
+    return X, Y
+
+def load_extra_datasets():
+    N = 200
+    noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
+    noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
+    blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
+    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)
+    no_structure = np.random.rand(N, 2), np.random.rand(N, 2)
+
+    return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure