Data Analysis and Machine Learning: Support Vector Machines

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%matplotlib inline

import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt

# Generate sample data
X = np.sort(5*np.random.rand(40,1), axis=0)
y = X**3
y=y.ravel()

# Add noise to targets
X[::4] +=3*(0.5 - np.random.rand(1))
y[::5] += 50 * (0.5 - np.random.rand(8))

plt.plot(X,y, 'g^')

#SVR Fit
svr_poly = SVR(kernel='poly', C=1e3, degree=3)
y_poly = svr_poly.fit(X, y).predict(X)

# Plots
z = np.arange(0, 5, 0.1)
t = z**3
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(z,z**3, 'r--', label='Cubic Function with No Noise')
lw = 2
plt.scatter(X, y, color='darkorange', label='Gaussian Cubic Noise')
plt.plot(X, y_poly, color='green', lw=lw, label='Polynomial model')
plt.xlabel('data')
plt.ylabel('target')
plt.title('Cubic Gaussian Distribution')
plt.legend()
plt.show()


    

    




/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
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