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DimensionReduction.py
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DimensionReduction.py
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"""
@ Filename: DimensionReduction.py
@ Author: Ryuk
@ Create Date: 2019-06-02
@ Update Date: 2019-06-06
@ Description: Implement DimensionReduction
"""
import numpy as np
import pickle
import preProcess
class PCA:
def __init__(self, norm_type="Standardization", rate=0.9):
self.norm_type = norm_type
self.matrix = None
self.contribute_rate = None
self.acc_contribute_rate = None
self.rate = rate
'''
Function: train
Description: train the model
Input: train_data dataType: ndarray description: features
Output: self dataType: obj description: the trained model
'''
def train(self, train_data):
# decentration
data = train_data - train_data.mean(axis=0)
# calculate the eigenvalue and eigenvector of covariance matrix
covariance_matrix = np.cov(data, rowvar=False)
eigenvalue, eigenvector = np.linalg.eig(covariance_matrix)
index = np.argsort(-eigenvalue)
eigenvalue = eigenvalue[index]
eigenvector = eigenvector[:, index]
# calculate contribute rate
contribute_rate = np.zeros(len(index))
acc_contribute_rate = np.zeros(len(index))
value_sum = eigenvalue.sum()
sum = 0
k = 0
for i in range(len(eigenvalue)):
sum = sum + eigenvalue[i]
contribute_rate[i] = eigenvalue[i]/value_sum
acc_contribute_rate[i] = sum/value_sum
if (acc_contribute_rate[i-1] < self.rate) and (acc_contribute_rate[i] >= self.rate):
k = i
self.contribute_rate = contribute_rate
self.acc_contribute_rate = acc_contribute_rate
matrix = np.mat(eigenvector)[:, k]
self.matrix = matrix
return self
'''
Function: transformData
Description: transform data
Input: data dataType: ndarray description: original data
Output: transformed_data dataType: ndarray description: transformed data
'''
def transformData(self, data):
data = data - data.mean(axis=0)
transformed_data = np.dot(data, self.matrix)
return transformed_data
'''
Function: save
Description: save the model as pkl
Input: filename dataType: str description: the path to save model
'''
def save(self, filename):
f = open(filename, 'w')
pickle.dump(self.matrix, f)
f.close()
'''
Function: load
Description: load the model
Input: filename dataType: str description: the path to save model
Output: self dataType: obj description: the trained model
'''
def load(self, filename):
f = open(filename)
self.matrix = pickle.load(f)
return self
class LDA:
def __init__(self, norm_type="Standardization", rate=0.9):
self.norm_type = norm_type
self.matrix = None
self.contribute_rate = None
self.acc_contribute_rate = None
self.rate = rate
'''
Function: train
Description: train the model
Input: train_data dataType: ndarray description: features
Output: self dataType: obj description: the trained model
'''
def train(self, data, label):
# Normalization
if self.norm_type == "Standardization":
data = preProcess.Standardization(data)
else:
data = preProcess.Normalization(data)
unique_label = np.unique(label)
mu = np.mean(data, axis=0)
# St = np.dot((data - mu).T, data - mu)
Sw = 0
Sb = 0
for c in unique_label:
index = np.where(label == c)
Ni = len(index)
xi = data[index]
mui = np.mean(xi, axis=0)
# calculate Sw
Si = np.dot((xi - mui).T, xi - mui)
Sw = Sw + Si
# calculate Sb
delta = np.expand_dims(mu - mui, axis=1)
Sb = Sb + Ni * np.dot(delta, delta.T)
# calculate the eigenvalue, eigenvector of Sw-1 * Sb
temp = np.dot(np.linalg.inv(Sw), Sb)
eigenvalue, eigenvector = np.linalg.eig(np.dot(np.linalg.inv(Sw), Sb))
index = np.argsort(-eigenvalue)
eigenvalue = eigenvalue[index]
eigenvector = eigenvector[:, index]
# calculate contribute rate
contribute_rate = np.zeros(len(index))
acc_contribute_rate = np.zeros(len(index))
value_sum = eigenvalue.sum()
sum = 0
k = 0
for i in range(len(eigenvalue)):
sum = sum + eigenvalue[i]
contribute_rate[i] = eigenvalue[i] / value_sum
acc_contribute_rate[i] = sum / value_sum
if (acc_contribute_rate[i - 1] < self.rate) and (acc_contribute_rate[i] >= self.rate):
k = i
self.contribute_rate = contribute_rate
self.acc_contribute_rate = acc_contribute_rate
matrix = np.mat(eigenvector)[:, k]
self.matrix = matrix
return self
'''
Function: transformData
Description: transform data
Input: data dataType: ndarray description: original data
Output: transformed_data dataType: ndarray description: transformed data
'''
def transformData(self, data):
transformed_data = np.dot(data, self.matrix)
return transformed_data