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from __future__ import division, print_function
import numpy as np
import cvxopt
from mlfromscratch.utils import train_test_split, normalize, accuracy_score
from mlfromscratch.utils.kernels import *
from mlfromscratch.utils import Plot
# Hide cvxopt output
cvxopt.solvers.options['show_progress'] = False
class SupportVectorMachine(object):
"""The Support Vector Machine classifier.
Uses cvxopt to solve the quadratic optimization problem.
C: float
Penalty term.
kernel: function
Kernel function. Can be either polynomial, rbf or linear.
power: int
The degree of the polynomial kernel. Will be ignored by the other
kernel functions.
gamma: float
Used in the rbf kernel function.
coef: float
Bias term used in the polynomial kernel function.
def __init__(self, C=1, kernel=rbf_kernel, power=4, gamma=None, coef=4):
self.C = C
self.kernel = kernel
self.power = power
self.gamma = gamma
self.coef = coef
self.lagr_multipliers = None
self.support_vectors = None
self.support_vector_labels = None
self.intercept = None
def fit(self, X, y):
n_samples, n_features = np.shape(X)
# Set gamma to 1/n_features by default
if not self.gamma:
self.gamma = 1 / n_features
# Initialize kernel method with parameters
self.kernel = self.kernel(
# Calculate kernel matrix
kernel_matrix = np.zeros((n_samples, n_samples))
for i in range(n_samples):
for j in range(n_samples):
kernel_matrix[i, j] = self.kernel(X[i], X[j])
# Define the quadratic optimization problem
P = cvxopt.matrix(np.outer(y, y) * kernel_matrix, tc='d')
q = cvxopt.matrix(np.ones(n_samples) * -1)
A = cvxopt.matrix(y, (1, n_samples), tc='d')
b = cvxopt.matrix(0, tc='d')
if not self.C:
G = cvxopt.matrix(np.identity(n_samples) * -1)
h = cvxopt.matrix(np.zeros(n_samples))
G_max = np.identity(n_samples) * -1
G_min = np.identity(n_samples)
G = cvxopt.matrix(np.vstack((G_max, G_min)))
h_max = cvxopt.matrix(np.zeros(n_samples))
h_min = cvxopt.matrix(np.ones(n_samples) * self.C)
h = cvxopt.matrix(np.vstack((h_max, h_min)))
# Solve the quadratic optimization problem using cvxopt
minimization = cvxopt.solvers.qp(P, q, G, h, A, b)
# Lagrange multipliers
lagr_mult = np.ravel(minimization['x'])
# Extract support vectors
# Get indexes of non-zero lagr. multipiers
idx = lagr_mult > 1e-7
# Get the corresponding lagr. multipliers
self.lagr_multipliers = lagr_mult[idx]
# Get the samples that will act as support vectors
self.support_vectors = X[idx]
# Get the corresponding labels
self.support_vector_labels = y[idx]
# Calculate intercept with first support vector
self.intercept = self.support_vector_labels[0]
for i in range(len(self.lagr_multipliers)):
self.intercept -= self.lagr_multipliers[i] * self.support_vector_labels[
i] * self.kernel(self.support_vectors[i], self.support_vectors[0])
def predict(self, X):
y_pred = []
# Iterate through list of samples and make predictions
for sample in X:
prediction = 0
# Determine the label of the sample by the support vectors
for i in range(len(self.lagr_multipliers)):
prediction += self.lagr_multipliers[i] * self.support_vector_labels[
i] * self.kernel(self.support_vectors[i], sample)
prediction += self.intercept
return np.array(y_pred)
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