def get_covariance_matrix(data):
avg=get_average(data)
avg=[n[0] for n in avg]
centralized_matrix=[0]*len(data)
for i,item in enumerate(data):
centralized_matrix[i]=Vector.minus(item,avg)
res=Matrix.multiply(Matrix.transpose(centralized_matrix),centralized_matrix)
N=len(data)-1
return Matrix.multiply_integer(res, 1/N)
def iteration(self):
A=Matrix.multiply(self.data_X, self.weights)
E=Matrix.minus(self.g_function(A),self.data_Y)
temp=Matrix.multiply_integer(Matrix.multiply(Matrix.transpose(self.data_X), E), self.learning_rate)
self.weights=Matrix.minus(self.weights,temp)
def test_multiply_integer(self):
A=[[1,2,3],[4,5,6]]
N=2
expected_result=[[2,4,6],[8,10,12]]
actual_result=Matrix.multiply_integer(A,N)
self.assertEqual(expected_result, actual_result)