eriklindernoren--ml-from-scratch
eriklindernoren
76 行
2.2 KiB
Python
76 行
2.2 KiB
Python
from __future__ import division
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import numpy as np
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import math
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import sys
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def calculate_entropy(y):
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""" Calculate the entropy of label array y """
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log2 = lambda x: math.log(x) / math.log(2)
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unique_labels = np.unique(y)
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entropy = 0
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for label in unique_labels:
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count = len(y[y == label])
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p = count / len(y)
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entropy += -p * log2(p)
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return entropy
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def mean_squared_error(y_true, y_pred):
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""" Returns the mean squared error between y_true and y_pred """
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mse = np.mean(np.power(y_true - y_pred, 2))
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return mse
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def calculate_variance(X):
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""" Return the variance of the features in dataset X """
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mean = np.ones(np.shape(X)) * X.mean(0)
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n_samples = np.shape(X)[0]
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variance = (1 / n_samples) * np.diag((X - mean).T.dot(X - mean))
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return variance
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def calculate_std_dev(X):
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""" Calculate the standard deviations of the features in dataset X """
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std_dev = np.sqrt(calculate_variance(X))
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return std_dev
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def euclidean_distance(x1, x2):
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""" Calculates the l2 distance between two vectors """
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distance = 0
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# Squared distance between each coordinate
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for i in range(len(x1)):
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distance += pow((x1[i] - x2[i]), 2)
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return math.sqrt(distance)
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def accuracy_score(y_true, y_pred):
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""" Compare y_true to y_pred and return the accuracy """
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accuracy = np.sum(y_true == y_pred, axis=0) / len(y_true)
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return accuracy
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def calculate_covariance_matrix(X, Y=None):
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""" Calculate the covariance matrix for the dataset X """
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if Y is None:
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Y = X
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n_samples = np.shape(X)[0]
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covariance_matrix = (1 / (n_samples-1)) * (X - X.mean(axis=0)).T.dot(Y - Y.mean(axis=0))
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return np.array(covariance_matrix, dtype=float)
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def calculate_correlation_matrix(X, Y=None):
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""" Calculate the correlation matrix for the dataset X """
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if Y is None:
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Y = X
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n_samples = np.shape(X)[0]
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covariance = (1 / n_samples) * (X - X.mean(0)).T.dot(Y - Y.mean(0))
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std_dev_X = np.expand_dims(calculate_std_dev(X), 1)
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std_dev_y = np.expand_dims(calculate_std_dev(Y), 1)
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correlation_matrix = np.divide(covariance, std_dev_X.dot(std_dev_y.T))
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return np.array(correlation_matrix, dtype=float)
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