mirror of
https://github.com/gsi-upm/sitc
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75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from math import cos, sin
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from scipy.constants import golden, pi
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def gen_spiral_dataset(n_examples=500, n_classes=2, a=None, b=None, pi_space=3):
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n_spirals = n_classes
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# default: golden spiral
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if a is None:
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a = golden
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if b is None:
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b = 2/pi
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theta = np.linspace(0,pi_space*pi, num=n_examples)
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xy = np.zeros((n_examples,2))
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# logaritmic spirals
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x_golden_parametric = lambda a, b, theta: a**(theta*b) * cos(theta)
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y_golden_parametric = lambda a, b, theta: a**(theta*b) * sin(theta)
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x_golden_parametric = np.vectorize(x_golden_parametric)
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y_golden_parametric = np.vectorize(y_golden_parametric)
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# rotation matrix
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gen_rotation = lambda theta: np.array([[cos(theta), -sin(theta)],[sin(theta), cos(theta)]])
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# rotation angles
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rot_division = (2*pi) / n_spirals
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rot_thetas = [i * rot_division for i in range(n_spirals)]
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XY = np.zeros((2, n_examples, n_spirals))
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for i in range(n_spirals):
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x = x_golden_parametric(a, b, theta)
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y = y_golden_parametric(a, b, theta)
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xy = np.vstack((x,y))
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R = gen_rotation(rot_thetas[i])
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xy_ = np.dot(R.T, xy)
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XY[:,:,i] = xy_
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return XY
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def load_spiral_dataset(n_examples=300, n_classes=2):
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XY = gen_spiral_dataset(n_examples, n_classes)
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X_s = []
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y_s = []
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for i in range(XY.shape[2]):
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X = XY[:,:,i].T
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X_s.append(X)
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y = np.array([i] * XY.shape[1]).T
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y_s.append(y)
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X = np.vstack(X_s)
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y = np.hstack(y_s)
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return X, y
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def plot_dataset(X,y):
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cm = plt.cm.RdBu
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plt.scatter(X[:,0], X[:,1], c=y, cmap=cm, lw=.5, s=10)
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def plot_decision_surface(X, y, classifier):
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h = .02
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cm = plt.cm.RdBu
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])#[:, 1]
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z = z.reshape(xx.shape)
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plt.contourf(xx, yy, z, cmap=cm, alpha=.8)
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plot_dataset(X, y)
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