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cpw.py
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cpw.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Feb 9 19:53:28 2017
@author: abhishek
"""
import math
import random
import matplotlib.pyplot as plt
import time
import pandas as pd
import numpy as np
from sklearn.svm import LinearSVC
import cpw_plot
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPRegressor
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier, Perceptron
class NN:
def __init__(self, NI, NH, NO):
# number of nodes in layers
self.ni = NI + 1 # +1 for bias
self.nh = NH
self.no = NO
# initialize node-activations
self.ai, self.ah, self.ao = [], [], []
self.ai = [1.0] * self.ni
self.ah = [1.0] * self.nh
self.ao = [1.0] * self.no
# create node weight matrices
self.wi = makeMatrix(self.ni, self.nh)
self.wo = makeMatrix(self.nh, self.no)
# initialize node weights to random vals
randomizeMatrix(self.wi, -0.2, 0.2)
randomizeMatrix(self.wo, -2.0, 2.0)
# create last change in weights matrices for momentum
self.ci = makeMatrix(self.ni, self.nh)
self.co = makeMatrix(self.nh, self.no)
def runNN(self, inputs):
if len(inputs) != self.ni - 1:
print('incorrect number of inputs')
for i in range(self.ni - 1):
self.ai[i] = inputs[i]
for j in range(self.nh):
sum = 0.0
for i in range(self.ni):
sum += (self.ai[i] * self.wi[i][j])
self.ah[j] = sigmoid(sum)
for k in range(self.no):
sum = 0.0
for j in range(self.nh):
sum += (self.ah[j] * self.wo[j][k])
self.ao[k] = sigmoid(sum)
return self.ao
def backPropagate(self, targets, N, M):
# calc output deltas
# we want to find the instantaneous rate of change of ( error with respect to weight from node j to node k)
# output_delta is defined as an attribute of each ouput node. It is not the final rate we need.
# To get the final rate we must multiply the delta by the activation of the hidden layer node in question.
# This multiplication is done according to the chain rule as we are taking the derivative of the activation function
# of the ouput node.
# dE/dw[j][k] = (t[k] - ao[k]) * s'( SUM( w[j][k]*ah[j] ) ) * ah[j]
output_deltas = [0.0] * self.no
for k in range(self.no):
error = targets[k] - self.ao[k]
output_deltas[k] = error * dsigmoid(self.ao[k])
# update output weights
for j in range(self.nh):
for k in range(self.no):
# output_deltas[k] * self.ah[j] is the full derivative of dError/dweight[j][k]
change = output_deltas[k] * self.ah[j]
self.wo[j][k] += N * change + M * self.co[j][k]
self.co[j][k] = change
# calc hidden deltas
hidden_deltas = [0.0] * self.nh
for j in range(self.nh):
error = 0.0
for k in range(self.no):
error += output_deltas[k] * self.wo[j][k]
hidden_deltas[j] = error * dsigmoid(self.ah[j])
# update input weights
for i in range(self.ni):
for j in range(self.nh):
change = hidden_deltas[j] * self.ai[i]
# print 'activation',self.ai[i],'synapse',i,j,'change',change
self.wi[i][j] += N * change + M * self.ci[i][j]
self.ci[i][j] = change
# calc combined error
# 1/2 for differential convenience & **2 for modulus
error = 0.0
for k in range(len(targets)):
error = 0.5 * (targets[k] - self.ao[k]) ** 2
return error
def weights(self):
print('Input weights:')
for i in range(self.ni):
print(self.wi[i])
print
print('Output weights:')
for j in range(self.nh):
print(self.wo[j])
print('')
def test(self, patterns):
for p in patterns:
inputs = p[0]
self.runNN(inputs)
# print ('Inputs:', p[0], '-->', self.runNN(inputs), ' Target', p[1])
def train(self, patterns, max_iterations=1000, N=0.5, M=0.1):
for i in range(max_iterations):
for p in patterns:
inputs = p[0]
targets = p[1]
self.runNN(inputs)
error = self.backPropagate(targets, N, M)
# if i % 50 == 0:
# print ('Combined error', error)
self.test(patterns)
def fun(self, x):
return self.runNN(x)
def sigmoid(x):
return math.tanh(x)
# the derivative of the sigmoid function in terms of output
# proof here:
# http://www.math10.com/en/algebra/hyperbolic-functions/hyperbolic-functions.html
def dsigmoid(y):
return 1 - y ** 2
def makeMatrix(I, J, fill=0.0):
m = []
for i in range(I):
m.append([fill] * J)
return m
def randomizeMatrix(matrix, a, b):
for i in range(len(matrix)):
for j in range(len(matrix[0])):
matrix[i][j] = random.uniform(a, b)
def backProp(dat_backProp, inp_start, inp_end, inc, out_max):
# start = time.time()
myNN2 = NN(1, 5, 1)
myNN2.train(dat_backProp)
print(' ')
inp2 = []
out2 = []
x1 = np.arange(inp_start, inp_end, inc)
x = x1.reshape(-1, 1).tolist()
for i in x:
inp2.append(i)
k =[]
k.append( i[0]/inp_end )
y = myNN2.fun(k)
for j in range(len(y)):
y[j] *= out_max
out2.append(list(y))
# print(time.time() - start)
plt.plot(np.asarray(inp2), np.asarray(out2), 'b--', x1, cpw_plot.f2(x1,6), 'go')
plt.ylabel('Impedance')
plt.xlabel('a/b')
plt.title('cpw using BackPropagation b/h=0.1')
plt.show()
errorplot = abs(np.asarray(out2).T - cpw_plot.f2(x1,6)) / cpw_plot.f2(x1,6) * 100
max_error = max(errorplot[0])
print('BACK_PROP: ')
print("max error: ",max_error)
avg_error = 0
for i in errorplot[0]:
avg_error += i
avg_error = avg_error/len(errorplot[0])
print("average erro: ",avg_error)
plt.plot(x1, errorplot[0], 'r--')
plt.ylabel('absolute error')
plt.xlabel('a/b ')
plt.title('%error using BackPropagation')
plt.show()
x = [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85,0.9,0.95]
for i in x:
ou = myNN2.fun([i / 0.99])
print(ou[0] * 105.1679)
def inBuilt(inp_dat,out_dat, inp_start, inp_end, inc, out_max):
inp = []
out = []
for n in inp_dat:
inp.append([int(n[0]*10)])
for n in out_dat:
out.append(int(n*100))
classifiers = [
("LINEAR: ", linear_model.LinearRegression()),
('LOG-LBFGS: ', LogisticRegression(solver='lbfgs', max_iter=2000)),
('LOG-NEWTON: ', LogisticRegression(solver='newton-cg', max_iter=2000)),
('MLPCLAS-ADAM: ', MLPClassifier(solver='adam', max_iter=5000)),
('SGDREG: ', MLPRegressor(solver='lbfgs', max_iter=2000)),
]
clas = [
('SVC', LinearSVC(max_iter=2000)),
]
for name, clf in classifiers:
print(' ')
clf.fit(inp,out)
print(name,': ')
inp2 = []
out2 = []
x1 = np.arange(inp_start*10, inp_end*10, inc*10)
x = x1.reshape(-1, 1).tolist()
for i in x:
inp2.append(i[0]/10)
k = []
k.append(i[0])
y = clf.predict(k[0])/100 + 4
# for j in range(len(y)):
# y[j] *= out_max
out2.append(list(y))
# print(time.time() - start)
plt.plot(np.asarray(inp2), np.asarray(out2), 'b--', np.asarray(inp2), cpw_plot.f2(np.asarray(inp2),6), 'go')
plt.ylabel('Impedance')
plt.xlabel('a/b ')
plt.title('cpw using ' + name + ' b/h=0.1')
plt.show()
errorplot = abs(np.asarray(out2).T - cpw_plot.f2(np.asarray(inp2),6)) / cpw_plot.f2(np.asarray(inp2),6 ) * 100
max_error = max(errorplot[0])
print("max error: ", max_error)
avg_error = 0
for i in errorplot[0]:
avg_error += i
avg_error = avg_error / len(errorplot[0])
print("average erro: ", avg_error)
plt.plot(np.asarray(inp2), errorplot[0], 'r--')
plt.ylabel('absolute error')
plt.xlabel('a/b')
plt.title('%error using ' + name)
plt.show()
x = [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85,0.9,0.95]
for i in x:
ou = clf.predict(i * 10)
print(ou[0] / 100)
def main():
cpw_plot.plot()
inp_dat = pd.read_csv('train_cpw_in.csv')
out_dat = pd.read_csv('train_cpw_out.csv')
dat_backProp = pd.read_csv('train_cpw_BP.csv')
inp_start = 0.15
inp_end = 0.99
inc = 0.01
out_max = 105.167957681
backProp(dat_backProp, inp_start, inp_end, inc, out_max)
inBuilt(inp_dat,out_dat, inp_start, inp_end, inc, out_max)
if __name__ == "__main__":
main()