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sitc/ml3/spiral.py

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2017-03-27 17:48:22 +00:00
import numpy as np
import matplotlib.pyplot as plt
from math import cos, sin
from scipy.constants import golden, pi
def gen_spiral_dataset(n_examples=500, n_classes=2, a=None, b=None, pi_space=3):
n_spirals = n_classes
# default: golden spiral
if a is None:
a = golden
if b is None:
b = 2/pi
theta = np.linspace(0,pi_space*pi, num=n_examples)
xy = np.zeros((n_examples,2))
# logaritmic spirals
x_golden_parametric = lambda a, b, theta: a**(theta*b) * cos(theta)
y_golden_parametric = lambda a, b, theta: a**(theta*b) * sin(theta)
x_golden_parametric = np.vectorize(x_golden_parametric)
y_golden_parametric = np.vectorize(y_golden_parametric)
# rotation matrix
gen_rotation = lambda theta: np.array([[cos(theta), -sin(theta)],[sin(theta), cos(theta)]])
# rotation angles
rot_division = (2*pi) / n_spirals
rot_thetas = [i * rot_division for i in range(n_spirals)]
XY = np.zeros((2, n_examples, n_spirals))
for i in range(n_spirals):
x = x_golden_parametric(a, b, theta)
y = y_golden_parametric(a, b, theta)
xy = np.vstack((x,y))
R = gen_rotation(rot_thetas[i])
xy_ = np.dot(R.T, xy)
XY[:,:,i] = xy_
return XY
def load_spiral_dataset(n_examples=300, n_classes=2):
XY = gen_spiral_dataset(n_examples, n_classes)
X_s = []
y_s = []
for i in range(XY.shape[2]):
X = XY[:,:,i].T
X_s.append(X)
y = np.array([i] * XY.shape[1]).T
y_s.append(y)
X = np.vstack(X_s)
y = np.hstack(y_s)
return X, y
def plot_dataset(X,y):
cm = plt.cm.RdBu
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, h=0.02):
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cm = plt.cm.RdBu
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])#[:, 1]
z = z.reshape(xx.shape)
plt.contourf(xx, yy, z, cmap=cm, alpha=.8)
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plot_dataset(X, y)