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import numpy as np
from random import seed
from succinctly.datasets import linearly_separable, get_dataset
from succinctly.algorithms.smo_algorithm import SmoAlgorithm
def linear_kernel(x1, x2):
return, x2)
def compute_w(multipliers, X, y):
return np.sum(multipliers[i] * y[i] * X[i] for i in range(len(y)))
if __name__ == '__main__':
seed(5) # to have reproducible results
X_data, y_data = get_dataset(linearly_separable.get_training_examples)
smo = SmoAlgorithm(X_data, y_data, C=10, tol=0.001, kernel=linear_kernel, use_linear_optim=True)
w = compute_w(smo.alphas, X_data, y_data)
print('w = {}'.format(w))
# -smo.b because Platt uses the convention w.x-b=0
print('b = {}'.format(-smo.b))
# w = [0.4443664 1.1105648]
# b = -9.66268641132