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Perceptron.py
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Perceptron.py
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"""
@Filename: Perceptron.py
@Author: Danc1elion
@Create Date: 2019-04-30
@Update Date: 2019-05-03
@Description: Implement of perceptron.py
"""
import numpy as np
import preProcess
import pickle
import random
class PerceptronClassifier:
def __init__(self, norm_type="Normalization", iterations=500, learning_rate=0.01):
self.norm_type = norm_type
self.iterations = iterations
self.learning_rate = learning_rate
self.gradients = None
self.loss = None
self.w = None
self.b = None
self.prediction = None
self.probability = None
'''
Function: sigmoid
Description: sigmoid function
Input: x dataType: ndarray description: input vector
derivative dataType: bool description: whether to calculate the derivative of sigmoid
Output: output dataType: float description: output
'''
def sigmoid(self, x, derivative=False):
output = 1/(1 + np.exp(-x))
if derivative:
output = output * (1 - output)
return output
'''
Function: initializeParameter
Description: initialize parameter
Input: feature_dim dataType: int description: feature dimension
'''
def initializeParameter(self, feature_dim):
w = np.random.normal(0, 1, [feature_dim, 1])
b = 0
self.w = w
self.b = b
'''
Function: BackPropagate
Description: BackPropagate function
Input: w dataType: dict description: the weights in network
b dataType: dict description: the bias in network
train_data dataType: ndarray description: train data
train_label dataType: ndarray description: train label
Output: gradients dataType: dict description: gradients
cost dataType: float description: loss
'''
def backPropagate(self, train_data, train_label):
num = train_label.shape[0]
# forward
A = self.sigmoid(np.dot(train_data, self.w) + self.b)
cost = -1 / num * np.sum(train_label * np.log(A) + (1 - train_label) * np.log(1 - A))
# backward
dw = 1 / num * np.dot(train_data.T, A - train_label)
db = 1 / num * np.sum(A - train_label)
# save gradients
gradients = {"dw": dw,
"db": db}
return gradients, cost
'''
Function: train
Description: train the model
Input: train_data dataType: ndarray description: features
train_label dataType: ndarray description: labels
Output: self dataType: obj description: the trained model
'''
def train(self, train_data, train_label):
if self.norm_type == "Standardization":
train_data = preProcess.Standardization(train_data)
else:
train_data = preProcess.Normalization(train_data)
feature_dim = len(train_data[1])
train_label = np.expand_dims(train_label, axis=1)
self.initializeParameter(feature_dim)
self.loss = []
# training process
for i in range(self.iterations):
gradients, cost = self.backPropagate(train_data, train_label)
# get the derivative
dw = gradients["dw"]
db = gradients["db"]
# update parameter
self.w = self.w - self.learning_rate * dw
self.b = self.b - self.learning_rate * db
self.loss.append(cost)
return self
'''
Function: predict
Description: predict the testing set
Input: train_data dataType: ndarray description: features
prob dataType: bool description: return probaility of label
Output: prediction dataType: ndarray description: the prediction results for testing set
'''
def predict(self, test_data, prob="False"):
# Normalization
if self.norm_type == "Standardization":
test_data = preProcess.Standardization(test_data)
else:
test_data = preProcess.Normalization(test_data)
test_num = test_data.shape[0]
prediction = np.zeros([test_num, 1])
probability = np.zeros([test_num, 1])
for i in range(test_num):
probability[i] = self.sigmoid(np.dot(self.w.T, test_data[i, :]) + self.b) # prediction = self.sigmoid(np.dot(self.w.T, test_data) + self.b) can speed up
if probability[i] > 0:
prediction[i] = 1
else:
prediction[i] = -1
self.prediction = prediction
self.probability = probability
if prob:
return probability
else:
return prediction
'''
Function: accuracy
Description: show detection result
Input: test_label dataType: ndarray description: labels of test data
Output: accuracy dataType: float description: detection accuarcy
'''
def accuarcy(self, test_label):
test_label = np.expand_dims(test_label, axis=1)
prediction = self.prediction
accuarcy = sum(prediction == test_label)/len(test_label)
return accuarcy
'''
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')
model = {'w': self.w, 'b': self.b}
pickle.dump(model, 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)
model = pickle.load(f)
self.w = model['w']
self.b = model['b']
return self