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# SVM (Support Vector Machine) in Python - ML From Scratch 07

19 Nov 2019

In this Machine Learning from Scratch Tutorial, we are going to implement a SVM (Support Vector Machine) algorithm using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47

## Implementation

``````import numpy as np

class SVM:

def __init__(self, learning_rate=0.001, lambda_param=0.01, n_iters=1000):
self.lr = learning_rate
self.lambda_param = lambda_param
self.n_iters = n_iters
self.w = None
self.b = None

def fit(self, X, y):
n_samples, n_features = X.shape

y_ = np.where(y <= 0, -1, 1)

self.w = np.zeros(n_features)
self.b = 0

for _ in range(self.n_iters):
for idx, x_i in enumerate(X):
condition = y_[idx] * (np.dot(x_i, self.w) - self.b) >= 1
if condition:
self.w -= self.lr * (2 * self.lambda_param * self.w)
else:
self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_[idx]))
self.b -= self.lr * y_[idx]

def predict(self, X):
approx = np.dot(X, self.w) - self.b
return np.sign(approx)
``````