wehub-resource-sync
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from __future__ import print_function, division
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import numpy as np
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from mlfromscratch.utils import calculate_covariance_matrix, normalize, standardize
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class LDA():
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"""The Linear Discriminant Analysis classifier, also known as Fisher's linear discriminant.
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Can besides from classification also be used to reduce the dimensionaly of the dataset.
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"""
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def __init__(self):
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self.w = None
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def transform(self, X, y):
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self.fit(X, y)
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# Project data onto vector
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X_transform = X.dot(self.w)
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return X_transform
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def fit(self, X, y):
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# Separate data by class
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X1 = X[y == 0]
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X2 = X[y == 1]
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# Calculate the covariance matrices of the two datasets
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cov1 = calculate_covariance_matrix(X1)
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cov2 = calculate_covariance_matrix(X2)
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cov_tot = cov1 + cov2
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# Calculate the mean of the two datasets
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mean1 = X1.mean(0)
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mean2 = X2.mean(0)
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mean_diff = np.atleast_1d(mean1 - mean2)
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# Determine the vector which when X is projected onto it best separates the
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# data by class. w = (mean1 - mean2) / (cov1 + cov2)
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self.w = np.linalg.pinv(cov_tot).dot(mean_diff)
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def predict(self, X):
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y_pred = []
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for sample in X:
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h = sample.dot(self.w)
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y = 1 * (h < 0)
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y_pred.append(y)
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return y_pred
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