Divyosmi Goswami Divyosmi

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	<!DOCTYPE html>
	<html>
	<head>
	<title>k-means</title>
	</head>

	<body>
	<img src="https://upload.wikimedia.org/wikipedia/commons/e/ea/K-means_convergence.gif">
	</body>

	import numpy as np

	class perceptron:

	def init(self,x):

	self.x = x

	self.weight = np.random.rand(len(x[0])+1)

	# Make a prediction with weights

	def predict(row, weights):

	activation = weights[0] for i in range(len(row)-1):

	activation += weights[i + 1] * row[i]

	return 1.0 if activation >= 0.0 else 0.0

	def accuracy_metric(actual, predicted):

	correct = 0 for i in range(len(actual)):

	if actual[i] == predicted[i]:

	correct += 1

	return correct / float(len(actual)) * 100.0

	def train_weights(train, l_rate=0.001, n_epoch=500,verbose=True):

	weights = [0.0 for i in range(len(train[0]))]
	for epoch in range(n_epoch):

	sum_error = 0.0

	for row in train:

	prediction = predict(row, weights)

	# Make a prediction with weights

	def predict(row, weights):

	activation = weights[0]

	for i in range(len(row)-1):

	activation += weights[i + 1] * row[i]

	activation = sum(weight_i * x_i) + bias
	prediction = 1.0 if activation >= 0.0 else 0.0

	w is weight, learning rate is a hyper parameter that deals with the amount in which one has to tune it to update their weights and bias, expected is the value of the prediction and predicted is what you
	w = w + learning_rate * (expected - predicted) * x