# neuralnetbackprop.py
# uses Python version 2.7.8
import random
import math
# ------------------------------------
def show_data(matrix, num_first_rows):
#for i in range(len(matrix)):
for i in range(0, num_first_rows-1):
print "[" + str(i).rjust(2) + "]",
for j in range(len(matrix[i])):
print str("%.1f" % matrix[i][j]).rjust(5),
print "\n",
print "........"
last_row = len(matrix) - 1
print "[" + str(last_row).rjust(2) + "]",
for j in range(len(matrix[last_row])):
print str("%.1f" % matrix[last_row][j]).rjust(5),
print "\n"
def show_vector(vector):
for i in range(len(vector)):
if i % 8 == 0: # 8 columns
print "\n",
if vector[i] >= 0.0:
print '',
print "%.4f" % vector[i], # 4 decimals
print "\n"
# ------------------------------------
class NeuralNetwork:
def __init__(self, num_input, num_hidden, num_output):
self.num_input = num_input
self.num_hidden = num_hidden
self.num_output = num_output
self.inputs = [0 for i in range(num_input)]
self.ih_weights = self.make_matrix(num_input, num_hidden)
self.h_biases = [0 for i in range(num_hidden)]
self.h_outputs = [0 for i in range(num_hidden)]
self.ho_weights = self.make_matrix(num_hidden, num_output)
self.o_biases = [0 for i in range(num_output)]
self.outputs = [0 for i in range(num_output)]
# random.seed(0) # hidden function is 'normal' approach
self.rnd = random.Random(0) # allows multiple instances
self.initialize_weights()
def make_matrix(self, rows, cols):
result = [[0 for j in range(cols)] for i in range(rows)]
return result
def set_weights(self, weights):
k = 0
for i in range(self.num_input):
for j in range(self.num_hidden):
self.ih_weights[i][j] = weights[k]
k += 1
for i in range(self.num_hidden):
self.h_biases[i] = weights[k]
k += 1
for i in range(self.num_hidden):
for j in range(self.num_output):
self.ho_weights[i][j] = weights[k]
k += 1
for i in range(self.num_output):
self.o_biases[i] = weights[k]
k += 1
def get_weights(self):
num_wts = ((self.num_input * self.num_hidden) + self.num_hidden +
(self.num_hidden * self.num_output) + self.num_output)
result = [0 for i in range(num_wts)]
k = 0
for i in range(self.num_input):
for j in range(self.num_hidden):
result[k] = self.ih_weights[i][j]
k += 1
for i in range(self.num_hidden):
result[k] = self.h_biases[i]
k += 1
for i in range(self.num_hidden):
for j in range(self.num_output):
result[k] = self.ho_weights[i][j]
k += 1
for i in range(self.num_output):
result[k] = self.o_biases[i]
k += 1
return result
def initialize_weights(self):
num_wts = ((self.num_input * self.num_hidden) + self.num_hidden +
(self.num_hidden * self.num_output) + self.num_output)
wts = [0 for i in range(num_wts)]
lo = -0.01
hi = 0.01
for i in range(len(wts)):
wts[i] = (hi - lo) * self.rnd.random() + lo
self.set_weights(wts)
def compute_outputs(self, x_values):
h_sums = [0 for i in range(self.num_hidden)]
o_sums = [0 for i in range(self.num_output)]
for i in range(len(x_values)):
self.inputs[i] = x_values[i]
for j in range(self.num_hidden):
for i in range(self.num_input):
h_sums[j] += (self.inputs[i] * self.ih_weights[i][j])
for i in range(self.num_hidden):
h_sums[i] += self.h_biases[i]
for i in range(self.num_hidden):
self.h_outputs[i] = self.hypertan(h_sums[i])
for j in range(self.num_output):
for i in range(self.num_hidden):
o_sums[j] += (self.h_outputs[i] * self.ho_weights[i][j])
for i in range(self.num_output):
o_sums[i] += self.o_biases[i]
soft_out = self.softmax(o_sums)
for i in range(self.num_output):
self.outputs[i] = soft_out[i]
result = [0 for i in range(self.num_output)]
for i in range(self.num_output):
result[i] = self.outputs[i]
return result
def hypertan(self, x):
if x < -20.0:
return -1.0
elif x > 20.0:
return 1.0
else:
return math.tanh(x)
def softmaxnaive(self, o_sums):
div = 0
for i in range(len(o_sums)):
div = div + math.exp(o_sums[i])
result = [0 for i in range(len(o_sums))]
for i in range(len(o_sums)):
result[i] = math.exp(o_sums[i]) / div
return result
def softmax(self, o_sums):
m = max(o_sums)
scale = 0
for i in range(len(o_sums)):
scale = scale + (math.exp(o_sums[i] - m))
result = [0 for i in range(len(o_sums))]
for i in range(len(o_sums)):
result[i] = math.exp(o_sums[i] - m) / scale
return result
def train(self, train_data, max_epochs, learn_rate, momentum):
o_grads = [0 for i in range(self.num_output)] # gradients
h_grads = [0 for i in range(self.num_hidden)]
ih_prev_weights_delta = self.make_matrix(num_input, num_hidden) # momentum
h_prev_biases_delta = [0 for i in range(self.num_hidden)]
ho_prev_weights_delta = self.make_matrix(num_hidden, num_output)
o_prev_biases_delta = [0 for i in range(self.num_output)]
epoch = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
sequence = [i for i in range(len(train_data))]
while epoch < max_epochs:
self.rnd.shuffle(sequence)
for ii in range(len(train_data)):
idx = sequence[ii]
for j in range(self.num_input): # peel off x_values
x_values[j] = train_data[idx][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = train_data[idx][j + self.num_input]
self.compute_outputs(x_values) # outputs stored internally
# --- update-weights (back-prop) section
for i in range(self.num_output): # 1. compute output gradients
derivative = (1 - self.outputs[i]) * self.outputs[i]
o_grads[i] = derivative * (t_values[i] - self.outputs[i])
for i in range(self.num_hidden): # 2. compute hidden gradients
derivative = (1 - self.h_outputs[i]) * (1 + self.h_outputs[i])
sum = 0
for j in range(self.num_output):
x = o_grads[j] * self.ho_weights[i][j]
sum += x
h_grads[i] = derivative * sum
for i in range(self.num_input): # 3a. update input-hidden weights
for j in range(self.num_hidden):
delta = learn_rate * h_grads[j] * self.inputs[i]
self.ih_weights[i][j] += delta
self.ih_weights[i][j] += momentum * ih_prev_weights_delta[i][j] # momentum
ih_prev_weights_delta[i][j] = delta # save the delta for momentum
for i in range(self.num_hidden): # 3b. update hidden biases
delta = learn_rate * h_grads[i]
self.h_biases[i] += delta
self.h_biases[i] += momentum * h_prev_biases_delta[i]; # momentum
h_prev_biases_delta[i] = delta # save the delta
for i in range(self.num_hidden): # 4a. update hidden-output weights
for j in range(self.num_output):
delta = learn_rate * o_grads[j] * self.h_outputs[i]
self.ho_weights[i][j] += delta
self.ho_weights[i][j] += momentum * ho_prev_weights_delta[i][j]; # momentum
ho_prev_weights_delta[i][j] = delta # save
for i in range(self.num_output): # 4b. update output biases
delta = learn_rate * o_grads[i]
self.o_biases[i] += delta
self.o_biases[i] += momentum * o_prev_biases_delta[i] # momentum
o_prev_biases_delta[i] = delta # save
# --- end update-weights
epoch += 1
result = self.get_weights()
return result
def accuracy(self, data):
num_correct = 0
num_wrong = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
for i in range(len(data)):
for j in range(self.num_input): # peel off x_values
x_values[j] = data[i][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = data[i][j + self.num_input]
y_values = self.compute_outputs(x_values)
max_index = y_values.index(max(y_values))
if t_values[max_index] == 1.0:
num_correct += 1;
else:
num_wrong += 1;
return (num_correct * 1.0) / (num_correct + num_wrong)
# ------------------------------------
print "\nBegin neural network using Python demo"
print "\nGoal is to predict species from color, petal length, petal width \n"
print "The 30-item raw data looks like: \n"
print "[0] blue, 1.4, 0.3, setosa"
print "[1] pink, 4.9, 1.5, versicolor"
print "[2] teal, 5.6, 1.8, virginica"
print ". . ."
print "[29] pink, 5.9, 1.5, virginica"
train_data = ([[0 for j in range(7)]
for i in range(24)]) # 24 rows, 7 cols
train_data[0] = [ 1, 0, 1.4, 0.3, 1, 0, 0 ]
train_data[1] = [ 0, 1, 4.9, 1.5, 0, 1, 0 ]
train_data[2] = [ -1, -1, 5.6, 1.8, 0, 0, 1 ]
train_data[3] = [ -1, -1, 6.1, 2.5, 0, 0, 1 ]
train_data[4] = [ 1, 0, 1.3, 0.2, 1, 0, 0 ]
train_data[5] = [ 0, 1, 1.4, 0.2, 1, 0, 0 ]
train_data[6] = [ 1, 0, 6.6, 2.1, 0, 0, 1 ]
train_data[7] = [ 0, 1, 3.3, 1.0, 0, 1, 0 ]
train_data[8] = [ -1, -1, 1.7, 0.4, 1, 0, 0 ]
train_data[9] = [ 0, 1, 1.5, 0.1, 0, 1, 1 ]
train_data[10] = [ 0, 1, 1.4, 0.2, 1, 0, 0 ]
train_data[11] = [ 0, 1, 4.5, 1.5, 0, 1, 0 ]
train_data[12] = [ 1, 0, 1.4, 0.2, 1, 0, 0 ]
train_data[13] = [ -1, -1, 5.1, 1.9, 0, 0, 1 ]
train_data[14] = [ 1, 0, 6.0, 2.5, 0, 0, 1 ]
train_data[15] = [ 1, 0, 3.9, 1.4, 0, 1, 0 ]
train_data[16] = [ 0, 1, 4.7, 1.4, 0, 1, 0 ]
train_data[17] = [ -1, -1, 4.6, 1.5, 0, 1, 0 ]
train_data[18] = [ -1, -1, 4.5, 1.7, 0, 0, 1 ]
train_data[19] = [ 0, 1, 4.5, 1.3, 0, 1, 0 ]
train_data[20] = [ 1, 0, 1.5, 0.2, 1, 0, 0 ]
train_data[21] = [ 0, 1, 5.8, 2.2, 0, 0, 1 ]
train_data[22] = [ 0, 1, 4.0, 1.3, 0, 1, 0 ]
train_data[23] = [ -1, -1, 5.8, 1.8, 0, 0, 1 ]
test_data = ([[0 for j in range(7)]
for i in range(6)]) # 6 rows, 7 cols
test_data[0] = [ 1, 0, 1.5, 0.2, 1, 0, 0 ]
test_data[1] = [ -1, -1, 5.9, 2.1, 0, 0, 1 ]
test_data[2] = [ 0, 1, 1.4, 0.2, 1, 0, 0 ]
test_data[3] = [ 0, 1, 4.7, 1.6, 0, 1, 0 ]
test_data[4] = [ 1, 0, 4.6, 1.3, 0, 1, 0 ]
test_data[5] = [ 1, 0, 6.3, 1.8, 0, 0, 1 ]
print "\nFirst few lines of encoded training data are: \n"
show_data(train_data, 4)
print "\nThe encoded test data is: \n"
show_data(test_data, 5)
print "\nCreating a 4-input, 5-hidden, 3-output neural network"
print "Using tanh and softmax activations \n"
num_input = 4
num_hidden = 5
num_output = 3
nn = NeuralNetwork(num_input, num_hidden, num_output)
max_epochs = 70 # artificially small
learn_rate = 0.08 # artificially large
momentum = 0.01
print "Setting max_epochs = " + str(max_epochs)
print "Setting learn_rate = " + str(learn_rate)
print "Setting momentum = " + str(momentum)
print "\nBeginning training using back-propagation"
weights = nn.train(train_data, max_epochs, learn_rate, momentum)
print "Training complete \n"
print "Final neural network weights and bias values:"
show_vector(weights)
print "Model accuracy on training data =",
acc_train = nn.accuracy(train_data)
print "%.4f" % acc_train
print "Model accuracy on test data =",
acc_test = nn.accuracy(test_data)
print "%.4f" % acc_test
print "\nEnd back-prop demo \n"
Monday, July 4, 2016
Prediction using neural network in python
# neuralnetbackprop(Weather Forcasting using neural network)
# uses Python version 2.7.8
import random
import math
import csv, sys
from csv import DictReader
import sys
from scipy import optimize
from StringIO import StringIO
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
# ------------------------------------
def show_data(matrix, num_first_rows):
#for i in range(len(matrix)):
for i in range(0, num_first_rows-1):
print "[" + str(i).rjust(2) + "]",
for j in range(len(matrix[i])):
print str( matrix[i][j]).rjust(5),
print "\n",
print "........"
last_row = len(matrix) - 1
print "[" + str(last_row).rjust(2) + "]",
for j in range(len(matrix[last_row])):
print str( matrix[last_row][j]).rjust(5),
print "\n"
def show_vector(vector):
for i in range(len(vector)):
if i % 8 == 0: # 8 columns
print "\n",
if vector[i] >= 0.0:
print '',
print "%.4f" % vector[i], # 4 decimals
print "\n"
# ------------------------------------
class NeuralNetwork:
def __init__(self, num_input, num_hidden, num_output):
self.num_input = num_input
self.num_hidden = num_hidden
self.num_output = num_output
self.inputs = [0 for i in range(num_input)]
self.ih_weights = self.make_matrix(num_input, num_hidden)
self.h_biases = [0 for i in range(num_hidden)]
self.h_outputs = [0 for i in range(num_hidden)]
self.ho_weights = self.make_matrix(num_hidden, num_output)
self.o_biases = [0 for i in range(num_output)]
self.outputs = [0 for i in range(num_output)]
# random.seed(0) # hidden function is 'normal' approach
self.rnd = random.Random(0) # allows multiple instances
self.initialize_weights()
def make_matrix(self, rows, cols):
result = [[0 for j in range(cols)] for i in range(rows)]
return result
def set_weights(self, weights):
k = 0
for i in range(self.num_input):
for j in range(self.num_hidden):
self.ih_weights[i][j] = weights[k]
k += 1
for i in range(self.num_hidden):
self.h_biases[i] = weights[k]
k += 1
for i in range(self.num_hidden):
for j in range(self.num_output):
self.ho_weights[i][j] = weights[k]
k += 1
for i in range(self.num_output):
self.o_biases[i] = weights[k]
k += 1
def get_weights(self):
num_wts = ((self.num_input * self.num_hidden) + self.num_hidden +
(self.num_hidden * self.num_output) + self.num_output)
result = [0 for i in range(num_wts)]
k = 0
for i in range(self.num_input):
for j in range(self.num_hidden):
result[k] = self.ih_weights[i][j]
k += 1
for i in range(self.num_hidden):
result[k] = self.h_biases[i]
k += 1
for i in range(self.num_hidden):
for j in range(self.num_output):
result[k] = self.ho_weights[i][j]
k += 1
for i in range(self.num_output):
result[k] = self.o_biases[i]
k += 1
return result
def initialize_weights(self):
num_wts = ((self.num_input * self.num_hidden) + self.num_hidden +
(self.num_hidden * self.num_output) + self.num_output)
wts = [0 for i in range(num_wts)]
lo = -0.01
hi = 0.01
for i in range(len(wts)):
wts[i] = (hi - lo) * self.rnd.random() + lo
self.set_weights(wts)
def compute_outputs(self, x_values):
h_sums = [0 for i in range(self.num_hidden)]
o_sums = [0 for i in range(self.num_output)]
for i in range(len(x_values)):
self.inputs[i] = x_values[i]
for j in range(self.num_hidden):
for i in range(self.num_input):
h_sums[j] += (float(self.inputs[i]) * self.ih_weights[i][j])
for i in range(self.num_hidden):
h_sums[i] += self.h_biases[i]
for i in range(self.num_hidden):
self.h_outputs[i] = self.hypertan(h_sums[i])
for j in range(self.num_output):
for i in range(self.num_hidden):
o_sums[j] += (self.h_outputs[i] * self.ho_weights[i][j])
for i in range(self.num_output):
o_sums[i] += self.o_biases[i]
soft_out = self.softmax(o_sums)
for i in range(self.num_output):
self.outputs[i] = soft_out[i]
result = [0 for i in range(self.num_output)]
for i in range(self.num_output):
result[i] = self.outputs[i]
return result
def hypertan(self, x):
if x < -20.0:
return -1.0
elif x > 20.0:
return 1.0
else:
return math.tanh(x)
def softmaxnaive(self, o_sums):
div = 0
for i in range(len(o_sums)):
div = div + math.exp(o_sums[i])
result = [0 for i in range(len(o_sums))]
for i in range(len(o_sums)):
result[i] = math.exp(o_sums[i]) / div
return result
def softmax(self, o_sums):
m = max(o_sums)
scale = 0
for i in range(len(o_sums)):
scale = scale + (math.exp(o_sums[i] - m))
result = [0 for i in range(len(o_sums))]
for i in range(len(o_sums)):
result[i] = math.exp(o_sums[i] - m) / scale
return result
def train(self, train_data, max_epochs, learn_rate, momentum):
o_grads = [0 for i in range(self.num_output)] # gradients
h_grads = [0 for i in range(self.num_hidden)]
ih_prev_weights_delta = self.make_matrix(num_input, num_hidden) # momentum
h_prev_biases_delta = [0 for i in range(self.num_hidden)]
ho_prev_weights_delta = self.make_matrix(num_hidden, num_output)
o_prev_biases_delta = [0 for i in range(self.num_output)]
epoch = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
sequence = [i for i in range(len(train_data))]
while epoch < max_epochs:
self.rnd.shuffle(sequence)
for ii in range(len(train_data)):
idx = sequence[ii]
for j in range(self.num_input): # peel off x_values
x_values[j] = train_data[idx][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = train_data[idx][j + self.num_input]
self.compute_outputs(x_values) # outputs stored internally
# --- update-weights (back-prop) section
for i in range(self.num_output): # 1. compute output gradients
derivative = (1 - self.outputs[i]) * self.outputs[i]
o_grads[i] = derivative * (float(t_values[i]) - float(self.outputs[i]))
for i in range(self.num_hidden): # 2. compute hidden gradients
derivative = (1 - self.h_outputs[i]) * (1 + self.h_outputs[i])
sum = 0
for j in range(self.num_output):
x = o_grads[j] * self.ho_weights[i][j]
sum += x
h_grads[i] = derivative * sum
for i in range(self.num_input): # 3a. update input-hidden weights
for j in range(self.num_hidden):
delta = learn_rate * h_grads[j] * float(self.inputs[i])
self.ih_weights[i][j] += delta
self.ih_weights[i][j] += momentum * ih_prev_weights_delta[i][j] # momentum
ih_prev_weights_delta[i][j] = delta # save the delta for momentum
for i in range(self.num_hidden): # 3b. update hidden biases
delta = learn_rate * h_grads[i]
self.h_biases[i] += delta
self.h_biases[i] += momentum * h_prev_biases_delta[i]; # momentum
h_prev_biases_delta[i] = delta # save the delta
for i in range(self.num_hidden): # 4a. update hidden-output weights
for j in range(self.num_output):
delta = learn_rate * o_grads[j] * self.h_outputs[i]
self.ho_weights[i][j] += delta
self.ho_weights[i][j] += momentum * ho_prev_weights_delta[i][j]; # momentum
ho_prev_weights_delta[i][j] = delta # save
for i in range(self.num_output): # 4b. update output biases
delta = learn_rate * o_grads[i]
self.o_biases[i] += delta
self.o_biases[i] += momentum * o_prev_biases_delta[i] # momentum
o_prev_biases_delta[i] = delta # save
# --- end update-weights
epoch += 1
result = self.get_weights()
return result
def accuracy(self, data):
num_correct = 0
num_wrong = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
for i in range(len(data)):
for j in range(self.num_input): # peel off x_values
x_values[j] = data[i][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = data[i][j + self.num_input]
y_values = self.compute_outputs(x_values)
max_index = y_values.index(max(y_values))
if (float(t_values[max_index]) < 0.85) & (float(t_values[max_index]) > 0.15):
num_correct += 1;
else:
num_wrong += 1;
return (num_correct * 1.0) / (num_correct + num_wrong)
def result(self, data):
num_correct = 0
num_wrong = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
result = ""
for i in range(len(data)):
for j in range(self.num_input): # peel off x_values
x_values[j] = data[i][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = data[i][j + self.num_input]
y_values = self.compute_outputs(x_values)
for i in range(len(y_values)):
result += str(y_values[i])+","
return result
# ------------------------------------
print "\nNeural network using Python"
print "\nGoal is to predict Wind Data \n"
print "The 30-item raw data looks like: \n"
print "[0] Anuradhapura, 2006, Jan, 3.5"
print "[1] Anuradhapura, 2006, Feb, 3.5"
print "[2] Anuradhapura, 2006, Mar, 3.1"
print ". . ."
print "[29] Batticaloa, 2006, June, 6.0"
train_data = ([[0 for j in range(37)]
for i in range(24)]) # 24 rows, 7 cols
train_data[0] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.13]
train_data[1] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.13]
train_data[2] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.11]
train_data[3] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.17]
train_data[4] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.3]
train_data[5] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.33]
train_data[6] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.36]
train_data[7] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.35]
train_data[8] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.32]
train_data[9] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.19]
train_data[10] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.09]
train_data[11] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.13]
train_data[12] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.21]
train_data[13] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.22]
train_data[14] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,1.1]
train_data[15] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.19]
train_data[16] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.29]
train_data[17] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.34]
train_data[18] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.49]
train_data[19] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.35]
train_data[20] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.39]
train_data[21] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.22]
train_data[22] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.15]
train_data[23] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.3]
test_data = ([[0 for j in range(37)]
for i in range(12)]) # 6 rows, 37 cols
test_data[0] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,0,0,0,1,0.17]
test_data[1] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,0,0,1,0,0.13]
test_data[2] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,0,1,0,0,0.14]
test_data[3] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,1,0,0,0,0.38]
test_data[4] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,1,0,0,0,0,0.22]
test_data[5] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,1,0,0,0,0,0,0.32]
test_data[6] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,1,0,0,0,0,0,0,0.34]
test_data[7] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,1,0,0,0,0,0,0,0,0.29]
test_data[8] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,1,0,0,0,0,0,0,0,0,0.25]
test_data[9] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,1,0,0,0,0,0,0,0,0,0,0.12]
test_data[10] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,1,0,0,0,0,0,0,0,0,0,0,0.09]
test_data[11] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,1,0,0,0,0,0,0,0,0,0,0,0,0.13]
print "\nFirst few lines of encoded training data are: \n"
show_data(train_data, 4)
print "\nThe encoded test data is: \n"
show_data(test_data, 5)
print "\nCreating a 36-input, 37-hidden, 1-output neural network"
print "Using tanh and softmax activations \n"
num_input = 36
num_hidden = 37
num_output = 1
nn = NeuralNetwork(num_input, num_hidden, num_output)
max_epochs = 70 # artificially small
learn_rate = 0.08 # artificially large
momentum = 0.01
print "Setting max_epochs = " + str(max_epochs)
print "Setting learn_rate = " + str(learn_rate)
print "Setting momentum = " + str(momentum)
print "\nBeginning training using back-propagation"
weights = nn.train(train_data, max_epochs, learn_rate, momentum)
print "Training complete \n"
print "Final neural network weights and bias values:"
show_vector(weights)
print "Model accuracy on training data =",
acc_train = nn.accuracy(train_data)
print "%.4f" % acc_train
print "Model accuracy on test data =",
acc_test = nn.accuracy(test_data)
print "%.4f" % acc_test
print "---------------------------------------"
print "TESTING"
output = nn.result(test_data)
print output
filename2= 'datafile.csv'
with open(filename2,'rb') as f:
clm=[row["a"] for row in DictReader(f)]
x=[]
for i in clm:
x.append(float(i))
with open(filename2,'rb') as f:
clm1=[row["b"] for row in DictReader(f)]
y=[]
for i in clm1:
y.append(float(i))
with open(filename2,'rb') as f:
clm2=[row["c"] for row in DictReader(f)]
z=[]
for i in clm2:
z.append(float(i))
#wireframe plot
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
ax1.plot_wireframe(x,y,z)
plt.show()
#reader=csv.reader(f,delimiter=';')
#for row in reader:
#print row[0]
print "\nEnd back-prop demo \n"
# uses Python version 2.7.8
import random
import math
import csv, sys
from csv import DictReader
import sys
from scipy import optimize
from StringIO import StringIO
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
# ------------------------------------
def show_data(matrix, num_first_rows):
#for i in range(len(matrix)):
for i in range(0, num_first_rows-1):
print "[" + str(i).rjust(2) + "]",
for j in range(len(matrix[i])):
print str( matrix[i][j]).rjust(5),
print "\n",
print "........"
last_row = len(matrix) - 1
print "[" + str(last_row).rjust(2) + "]",
for j in range(len(matrix[last_row])):
print str( matrix[last_row][j]).rjust(5),
print "\n"
def show_vector(vector):
for i in range(len(vector)):
if i % 8 == 0: # 8 columns
print "\n",
if vector[i] >= 0.0:
print '',
print "%.4f" % vector[i], # 4 decimals
print "\n"
# ------------------------------------
class NeuralNetwork:
def __init__(self, num_input, num_hidden, num_output):
self.num_input = num_input
self.num_hidden = num_hidden
self.num_output = num_output
self.inputs = [0 for i in range(num_input)]
self.ih_weights = self.make_matrix(num_input, num_hidden)
self.h_biases = [0 for i in range(num_hidden)]
self.h_outputs = [0 for i in range(num_hidden)]
self.ho_weights = self.make_matrix(num_hidden, num_output)
self.o_biases = [0 for i in range(num_output)]
self.outputs = [0 for i in range(num_output)]
# random.seed(0) # hidden function is 'normal' approach
self.rnd = random.Random(0) # allows multiple instances
self.initialize_weights()
def make_matrix(self, rows, cols):
result = [[0 for j in range(cols)] for i in range(rows)]
return result
def set_weights(self, weights):
k = 0
for i in range(self.num_input):
for j in range(self.num_hidden):
self.ih_weights[i][j] = weights[k]
k += 1
for i in range(self.num_hidden):
self.h_biases[i] = weights[k]
k += 1
for i in range(self.num_hidden):
for j in range(self.num_output):
self.ho_weights[i][j] = weights[k]
k += 1
for i in range(self.num_output):
self.o_biases[i] = weights[k]
k += 1
def get_weights(self):
num_wts = ((self.num_input * self.num_hidden) + self.num_hidden +
(self.num_hidden * self.num_output) + self.num_output)
result = [0 for i in range(num_wts)]
k = 0
for i in range(self.num_input):
for j in range(self.num_hidden):
result[k] = self.ih_weights[i][j]
k += 1
for i in range(self.num_hidden):
result[k] = self.h_biases[i]
k += 1
for i in range(self.num_hidden):
for j in range(self.num_output):
result[k] = self.ho_weights[i][j]
k += 1
for i in range(self.num_output):
result[k] = self.o_biases[i]
k += 1
return result
def initialize_weights(self):
num_wts = ((self.num_input * self.num_hidden) + self.num_hidden +
(self.num_hidden * self.num_output) + self.num_output)
wts = [0 for i in range(num_wts)]
lo = -0.01
hi = 0.01
for i in range(len(wts)):
wts[i] = (hi - lo) * self.rnd.random() + lo
self.set_weights(wts)
def compute_outputs(self, x_values):
h_sums = [0 for i in range(self.num_hidden)]
o_sums = [0 for i in range(self.num_output)]
for i in range(len(x_values)):
self.inputs[i] = x_values[i]
for j in range(self.num_hidden):
for i in range(self.num_input):
h_sums[j] += (float(self.inputs[i]) * self.ih_weights[i][j])
for i in range(self.num_hidden):
h_sums[i] += self.h_biases[i]
for i in range(self.num_hidden):
self.h_outputs[i] = self.hypertan(h_sums[i])
for j in range(self.num_output):
for i in range(self.num_hidden):
o_sums[j] += (self.h_outputs[i] * self.ho_weights[i][j])
for i in range(self.num_output):
o_sums[i] += self.o_biases[i]
soft_out = self.softmax(o_sums)
for i in range(self.num_output):
self.outputs[i] = soft_out[i]
result = [0 for i in range(self.num_output)]
for i in range(self.num_output):
result[i] = self.outputs[i]
return result
def hypertan(self, x):
if x < -20.0:
return -1.0
elif x > 20.0:
return 1.0
else:
return math.tanh(x)
def softmaxnaive(self, o_sums):
div = 0
for i in range(len(o_sums)):
div = div + math.exp(o_sums[i])
result = [0 for i in range(len(o_sums))]
for i in range(len(o_sums)):
result[i] = math.exp(o_sums[i]) / div
return result
def softmax(self, o_sums):
m = max(o_sums)
scale = 0
for i in range(len(o_sums)):
scale = scale + (math.exp(o_sums[i] - m))
result = [0 for i in range(len(o_sums))]
for i in range(len(o_sums)):
result[i] = math.exp(o_sums[i] - m) / scale
return result
def train(self, train_data, max_epochs, learn_rate, momentum):
o_grads = [0 for i in range(self.num_output)] # gradients
h_grads = [0 for i in range(self.num_hidden)]
ih_prev_weights_delta = self.make_matrix(num_input, num_hidden) # momentum
h_prev_biases_delta = [0 for i in range(self.num_hidden)]
ho_prev_weights_delta = self.make_matrix(num_hidden, num_output)
o_prev_biases_delta = [0 for i in range(self.num_output)]
epoch = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
sequence = [i for i in range(len(train_data))]
while epoch < max_epochs:
self.rnd.shuffle(sequence)
for ii in range(len(train_data)):
idx = sequence[ii]
for j in range(self.num_input): # peel off x_values
x_values[j] = train_data[idx][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = train_data[idx][j + self.num_input]
self.compute_outputs(x_values) # outputs stored internally
# --- update-weights (back-prop) section
for i in range(self.num_output): # 1. compute output gradients
derivative = (1 - self.outputs[i]) * self.outputs[i]
o_grads[i] = derivative * (float(t_values[i]) - float(self.outputs[i]))
for i in range(self.num_hidden): # 2. compute hidden gradients
derivative = (1 - self.h_outputs[i]) * (1 + self.h_outputs[i])
sum = 0
for j in range(self.num_output):
x = o_grads[j] * self.ho_weights[i][j]
sum += x
h_grads[i] = derivative * sum
for i in range(self.num_input): # 3a. update input-hidden weights
for j in range(self.num_hidden):
delta = learn_rate * h_grads[j] * float(self.inputs[i])
self.ih_weights[i][j] += delta
self.ih_weights[i][j] += momentum * ih_prev_weights_delta[i][j] # momentum
ih_prev_weights_delta[i][j] = delta # save the delta for momentum
for i in range(self.num_hidden): # 3b. update hidden biases
delta = learn_rate * h_grads[i]
self.h_biases[i] += delta
self.h_biases[i] += momentum * h_prev_biases_delta[i]; # momentum
h_prev_biases_delta[i] = delta # save the delta
for i in range(self.num_hidden): # 4a. update hidden-output weights
for j in range(self.num_output):
delta = learn_rate * o_grads[j] * self.h_outputs[i]
self.ho_weights[i][j] += delta
self.ho_weights[i][j] += momentum * ho_prev_weights_delta[i][j]; # momentum
ho_prev_weights_delta[i][j] = delta # save
for i in range(self.num_output): # 4b. update output biases
delta = learn_rate * o_grads[i]
self.o_biases[i] += delta
self.o_biases[i] += momentum * o_prev_biases_delta[i] # momentum
o_prev_biases_delta[i] = delta # save
# --- end update-weights
epoch += 1
result = self.get_weights()
return result
def accuracy(self, data):
num_correct = 0
num_wrong = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
for i in range(len(data)):
for j in range(self.num_input): # peel off x_values
x_values[j] = data[i][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = data[i][j + self.num_input]
y_values = self.compute_outputs(x_values)
max_index = y_values.index(max(y_values))
if (float(t_values[max_index]) < 0.85) & (float(t_values[max_index]) > 0.15):
num_correct += 1;
else:
num_wrong += 1;
return (num_correct * 1.0) / (num_correct + num_wrong)
def result(self, data):
num_correct = 0
num_wrong = 0
x_values = [0 for i in range(self.num_input)]
t_values = [0 for i in range(self.num_output)]
result = ""
for i in range(len(data)):
for j in range(self.num_input): # peel off x_values
x_values[j] = data[i][j]
for j in range(self.num_output): # peel off t_values
t_values[j] = data[i][j + self.num_input]
y_values = self.compute_outputs(x_values)
for i in range(len(y_values)):
result += str(y_values[i])+","
return result
# ------------------------------------
print "\nNeural network using Python"
print "\nGoal is to predict Wind Data \n"
print "The 30-item raw data looks like: \n"
print "[0] Anuradhapura, 2006, Jan, 3.5"
print "[1] Anuradhapura, 2006, Feb, 3.5"
print "[2] Anuradhapura, 2006, Mar, 3.1"
print ". . ."
print "[29] Batticaloa, 2006, June, 6.0"
train_data = ([[0 for j in range(37)]
for i in range(24)]) # 24 rows, 7 cols
train_data[0] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.13]
train_data[1] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.13]
train_data[2] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.11]
train_data[3] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.17]
train_data[4] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.3]
train_data[5] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.33]
train_data[6] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.36]
train_data[7] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.35]
train_data[8] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.32]
train_data[9] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.19]
train_data[10] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.09]
train_data[11] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.13]
train_data[12] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.21]
train_data[13] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.22]
train_data[14] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,1.1]
train_data[15] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.19]
train_data[16] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.29]
train_data[17] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.34]
train_data[18] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.49]
train_data[19] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.35]
train_data[20] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.39]
train_data[21] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.22]
train_data[22] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,0.00,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.15]
train_data[23] = [ 0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.00,0.00,0,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.3]
test_data = ([[0 for j in range(37)]
for i in range(12)]) # 6 rows, 37 cols
test_data[0] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,0,0,0,1,0.17]
test_data[1] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,0,0,1,0,0.13]
test_data[2] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,0,1,0,0,0.14]
test_data[3] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,0,1,0,0,0,0.38]
test_data[4] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,0,1,0,0,0,0,0.22]
test_data[5] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,0,1,0,0,0,0,0,0.32]
test_data[6] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,0,1,0,0,0,0,0,0,0.34]
test_data[7] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,0,1,0,0,0,0,0,0,0,0.29]
test_data[8] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,0,1,0,0,0,0,0,0,0,0,0.25]
test_data[9] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,0,1,0,0,0,0,0,0,0,0,0,0.12]
test_data[10] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,0,1,0,0,0,0,0,0,0,0,0,0,0.09]
test_data[11] = [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.89,1,0,0,0,0,0,0,0,0,0,0,0,0.13]
print "\nFirst few lines of encoded training data are: \n"
show_data(train_data, 4)
print "\nThe encoded test data is: \n"
show_data(test_data, 5)
print "\nCreating a 36-input, 37-hidden, 1-output neural network"
print "Using tanh and softmax activations \n"
num_input = 36
num_hidden = 37
num_output = 1
nn = NeuralNetwork(num_input, num_hidden, num_output)
max_epochs = 70 # artificially small
learn_rate = 0.08 # artificially large
momentum = 0.01
print "Setting max_epochs = " + str(max_epochs)
print "Setting learn_rate = " + str(learn_rate)
print "Setting momentum = " + str(momentum)
print "\nBeginning training using back-propagation"
weights = nn.train(train_data, max_epochs, learn_rate, momentum)
print "Training complete \n"
print "Final neural network weights and bias values:"
show_vector(weights)
print "Model accuracy on training data =",
acc_train = nn.accuracy(train_data)
print "%.4f" % acc_train
print "Model accuracy on test data =",
acc_test = nn.accuracy(test_data)
print "%.4f" % acc_test
print "---------------------------------------"
print "TESTING"
output = nn.result(test_data)
print output
filename2= 'datafile.csv'
with open(filename2,'rb') as f:
clm=[row["a"] for row in DictReader(f)]
x=[]
for i in clm:
x.append(float(i))
with open(filename2,'rb') as f:
clm1=[row["b"] for row in DictReader(f)]
y=[]
for i in clm1:
y.append(float(i))
with open(filename2,'rb') as f:
clm2=[row["c"] for row in DictReader(f)]
z=[]
for i in clm2:
z.append(float(i))
#wireframe plot
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
ax1.plot_wireframe(x,y,z)
plt.show()
#reader=csv.reader(f,delimiter=';')
#for row in reader:
#print row[0]
print "\nEnd back-prop demo \n"
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