Rearrange files

1 files changed, 0 insertions, 66 deletions

diff --git a/csci4511w/colab/HW5 - Neural Networks/planar_utils.py b/csci4511w/colab/HW5 - Neural Networks/planar_utils.py
deleted file mode 100644
index 1b22c39..0000000
--- a/csci4511w/colab/HW5 - Neural Networks/planar_utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-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