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This is a code for multiclass perceptron.
import numpy as nmpy #Importing Numpy library for matrix operation
#Creating a Perceptron Class with training and pocket algorithm testing function
class Create_Perceptron():
def __init__(self, vector, weight, label, learning, bias):
self.vector = vector
self.weight = weight
self.labels = label
self.current_accuracy = 0
self.pocket_weight = weight
self.learning = learning
self.bias = bias
def train(self):
y = 0
for k in range(0, 250):
for i in range(0,len(self.labels)):
accuracy = self.test(self.vector, self.weight, self.labels)
if(accuracy >= self.current_accuracy):
self.pocket_weight = self.weight
self.current_accuracy = accuracy
y = nmpy.dot(nmpy.transpose(self.weight),nmpy.transpose(nmpy.array([self.vector[i]]))) + self.bias
if(y >= 0):
y = 1
else:
y = 0
self.bias += (self.labels[i]-y)*self.learning
self.weight = self.weight + (self.labels[i]-y)*self.learning*self.vector[i].reshape(4, 1)
#print(self.current_accuracy)
def test(self, test_vector, weights, labels):
correct = 0
for i in range(0,len(labels)):
y = nmpy.dot(nmpy.transpose(weights),nmpy.transpose(nmpy.array([test_vector[i]])))
if(y >= 0):
y = 1
else:
y = 0
if(y == labels[i]):
correct += 1
return correct/len(labels)*100
def pocket_weight(self):
return self.pocket_weight
def bias(self):
return self.bias
def export_data(output_file, inp, vector):
arr = vector.split(',')
string = arr[0]+','+arr[1]+','+arr[2]+','+arr[3]
if(inp==2):
output_file.write((string+',Iris-setosa\n'))
elif(inp==0):
output_file.write((string+',Iris-versicolor\n'))
else:
output_file.write((string+',Iris-virginica\n'))
#Open the test data
#\ used to escape U from Users as it is a unicode character
file = open('C:\\Users\Shashi Suman\Desktop\Queens U\CISC 874\Assignment 1 ANN\iris_train.txt')
#Read the first line
line = file.readline()
label = []
weight = []
#Spliting the read line into array
arr = line.split(',')
#Creating 3 input sets for {Setosa, Versicolor}, {Versicolor, Virginica}, {Virginica, Setosa}
x1, x2, x3 = nmpy.empty((0, 4), dtype=float), nmpy.empty((0, 4), dtype=float), nmpy.empty((0, 4), dtype=float)
#Creating Label Vector
label_1, label_2, label_3 = nmpy.empty((0, 1)), nmpy.empty((0, 1)), nmpy.empty((0, 1))
#Reading each line
while line:
arr = line.split(',')
if(arr[4].strip()=='Iris-setosa'):
x1 = nmpy.append(x1, nmpy.array([[arr[0].strip(),arr[1].strip(),arr[2].strip(),arr[3].strip()]]), 0)
x3 = nmpy.append(x3, nmpy.array([[arr[0].strip(),arr[1].strip(),arr[2].strip(),arr[3].strip()]]) ,0)
if(label_1.size != 0):
label_1 = nmpy.append(label_1, nmpy.array([[0]]), 0)
else:
label_1 = nmpy.array([[0]])
if(label_3.size != 0):
label_3 = nmpy.append(label_3, nmpy.array([[1]]), 0)
else:
label_3 = nmpy.array([[1]])
elif(arr[4].strip()=='Iris-virginica'):
x3 = nmpy.append(x3, nmpy.array([[arr[0].strip(),arr[1].strip(),arr[2].strip(),arr[3].strip()]]), 0)
x2 = nmpy.append(x2, nmpy.array([[arr[0].strip(),arr[1].strip(),arr[2].strip(),arr[3].strip()]]), 0)
if(label_3.size != 0):
label_3 = nmpy.append(label_3, nmpy.array([[0]]), 0)
else:
label_3 = nmpy.array([[0]])
if(label_2.size != 0):
label_2 = nmpy.append(label_2, nmpy.array([[1]]), 0)
else:
label_2 = nmpy.array([[1]])
elif(arr[4].strip()=='Iris-versicolor'):
x1 = nmpy.append(x1, nmpy.array([[arr[0].strip(),arr[1].strip(),arr[2].strip(),arr[3].strip()]]), 0)
x2 = nmpy.append(x2, nmpy.array([[arr[0].strip(),arr[1].strip(),arr[2].strip(),arr[3].strip()]]), 0)
if(label_1.size != 0):
label_1 = nmpy.append(label_1, nmpy.array([[1]]), 0)
else:
label_1 = nmpy.array([[1]])
if(label_3.size != 0):
label_2 = nmpy.append(label_2, nmpy.array([[0]]), 0)
else:
label_2 = nmpy.array([[0]])
#print(len(x2),' ',len(label_2))
line = file.readline()
#Set Learning Rate
lr = 0.5
#Initialize weights with zeros
weight = nmpy.zeros([4, 1], dtype=float)
#Changing the data type of Input sets
x1 = x1.astype('float32')
x2 = x2.astype('float32')
x3 = x3.astype('float32')
#Changing the datatype of label sets
label_1 = label_1.astype('float32')
label_2 = label_2.astype('float32')
label_3 = label_3.astype('float32')
#Creating 3 Perceptron for each set. Passing input,weight, label, learning rate and bias
percep_1 = Create_Perceptron(x1, weight, label_1, lr, 0)
percep_2 = Create_Perceptron(x2, weight, label_2, lr, 40)
percep_3 = Create_Perceptron(x3, weight, label_3, lr, 0)
#Training begins for each instantiated perceptron
percep_1.train()
percep_2.train()
percep_3.train()
#Close the opened file
file.close()
#Open the test file
test_file = open('C:\\Users\Shashi Suman\Desktop\Queens U\CISC 874\Assignment 1 ANN\iris_test.txt')
#Open the Output File with write permission
output_file = open('C:\\Users\Shashi Suman\Desktop\Queens U\CISC 874\Assignment 1 ANN\\valid_output.txt','w')
#read the first line
line = test_file.readline()
#Accuracy Set to zero
accuracy_rate = 0;
length = 0
# Common Confusion Matrix
confusion = nmpy.zeros((4, 4))
confusion[0, 1] = 100 #Predicted Iris Setosa
confusion[0, 2] = 101 #Predicted Iris Versicolor
confusion[0, 3] = 110 #Predicted Iris Virginica
confusion[1, 0] = -100 #Actual Iris Setosa
confusion[2, 0] = -101 #Actual Iris Versicolor
confusion[3, 0] = -110 #Actual Iris Virginica
#TP-True Positive
TP = 0
#TN-True Negative
FN = 0
#FP-False Positive
FP = 0
TN = 0
Setosa_FN = 0
Setosa_TP = 0
Versicolor_FN = 0
Versicolor_TP = 0
Virginica_FN = 0
Virginica_TP = 0
Setosa_FP = 0
Versicolor_FP = 0
Virginica_FP = 0
#Reading Line from test data
while line:
arr = line.split(',')
test_vector = nmpy.array([[arr[0].strip(), arr[1].strip(), arr[2].strip(), arr[3].strip()]]).astype('float32')
#Dot Product for each set with trained Weights
f1 = nmpy.dot(nmpy.transpose(percep_1.pocket_weight),nmpy.transpose(test_vector)) + percep_1.bias
f2 = nmpy.dot(nmpy.transpose(percep_2.pocket_weight),nmpy.transpose(test_vector)) + percep_2.bias
f3 = nmpy.dot(nmpy.transpose(percep_3.pocket_weight),nmpy.transpose(test_vector)) + percep_3.bias
var = nmpy.argmax(nmpy.array([f1, f2, f3]))
######Confusion Matrix Design##########
if(var==2 and arr[4].strip()=='Iris-setosa'):
TP += 1
TN += 1
Setosa_TP += 1
confusion[1,1] += 1
elif(var==2 and arr[4].strip()=='Iris-versicolor'):
confusion[2, 1] += 1
FP += 1
FN += 1
elif(var==2 and arr[4].strip()=='Iris-virginica'):
confusion[3, 1] += 1
FP += 1
FN += 1
elif(var==0 and arr[4].strip()=='Iris-versicolor'):
TP += 1
TN += 1
Versicolor_TP += 1
confusion[2,2] += 1
elif(var==0 and arr[4].strip()=='Iris-setosa'):
confusion[1, 2] += 1
FP += 1
FN += 1
elif(var==0 and arr[4].strip()=='Iris-virginica'):
confusion[3, 2] += 1
FP += 1
FN += 1
elif(var==1 and arr[4].strip()=='Iris-virginica'):
TP += 1
TN += 1
confusion[3,3] += 1
Virginica_TP += 1
elif(var==1 and arr[4].strip()=='Iris-versicolor'):
confusion[2, 3] += 1
FP += 1
FN += 1
elif(var==1 and arr[4].strip()=='Iris-setosa'):
confusion[1, 3] += 1
FP += 1
FN += 1
####################################################
output_file.write('Iris-setosa\n')
#Output the data to file
if(var==2):
print('Iris-setosa')
export_data(output_file,var,line)
if(arr[4].strip()=='Iris-setosa'):
accuracy_rate += 1
elif(var==0):
print('Iris-versicolor')
export_data(output_file,var,line)
if(arr[4].strip()=='Iris-versicolor'):
accuracy_rate += 1
else:
print('Iris-virginica')
export_data(output_file,var,line)
if(arr[4].strip()=='Iris-virginica'):
accuracy_rate += 1
line = test_file.readline()
length += 1
#Updating Each Class TP (True Positive), FP (False Positive), FN (False Negative)
Setosa_FP = confusion[2, 1] + confusion[3, 1]
Versicolor_FP = confusion[1, 2] + confusion[3, 2]
Virginica_FP = confusion[1, 3] + confusion[2, 3]
Setosa_FN = confusion[1, 2] + confusion[1, 3]
Versicolor_FN = confusion[2, 1] + confusion[2, 3]
Virginica_FN = confusion[3, 1] + confusion[3, 2]
#Print the Confursion Matrix, Precision and Recall
print('\n##### Confusion Matrix_Testing Data #####')
print('100 --> Predicted Setosa 101 --> Predicted Versicolor 110 --> Predicted Virginica')
print('-100 --> Real Setosa -101 --> Real Versicolor -110 --> Real Virginica')
print(confusion)
print('\nThe Accuracy is: ',accuracy_rate/length*100)
print('The Overall Matrix Accuracy is: ',(TP+TN)/(TP+TN+FP+FN)*100)
print('\nRecall (Iris_Setosa)',Setosa_TP*100/(Setosa_TP + Setosa_FN))
print('Precision (Iris_Setosa)',Setosa_TP*100/(Setosa_TP + Setosa_FP))
print('\nRecall (Iris_Versicolor)',Versicolor_TP*100/(Versicolor_TP + Versicolor_FN))
print('Precision (Iris_Versicolor)',Versicolor_TP*100/(Versicolor_TP + Versicolor_FP))
print('\nRecall (Iris_Virginica)',Virginica_TP*100/(Virginica_TP + Virginica_FN))
print('Precision (Iris_Virginica)',Virginica_TP*100/(Virginica_TP + Virginica_FP))
print('#########################################################\n')
test_file.close()
output_file.close()
#ReOpen Training data for confusion matrix design
file = open('C:\\Users\Shashi Suman\Desktop\Queens U\CISC 874\Assignment 1 ANN\iris_train.txt')
line = file.readline()
#Initalize Confusion matrix with 4 X 4 zeros
confusion = nmpy.zeros((4, 4),dtype=int)
confusion = nmpy.zeros((4, 4))
confusion[0, 1] = 100 #Predicted Iris Setosa
confusion[0, 2] = 101 #Predicted Iris Versicolor
confusion[0, 3] = 110 #Predicted Iris Virginica
confusion[1, 0] = -100 #Actual Iris Setosa
confusion[2, 0] = -101 #Actual Iris Versicolor
confusion[3, 0] = -110 #Actual Iris Virginica
TP = 0
FN = 0
FP = 0
TP = 0
TN = 0
Setosa_FN = 0
Setosa_TP = 0
Versicolor_FN = 0
Versicolor_TP = 0
Virginica_FN = 0
Virginica_TP = 0
Setosa_FP = 0
Versicolor_FP = 0
Virginica_FP = 0
while line:
arr = line.split(',')
test_vector = nmpy.array([[arr[0].strip(), arr[1].strip(), arr[2].strip(), arr[3].strip()]]).astype('float32')
#Dor Product for each set
f1 = nmpy.dot(nmpy.transpose(percep_1.pocket_weight),nmpy.transpose(test_vector))
f2 = nmpy.dot(nmpy.transpose(percep_2.pocket_weight),nmpy.transpose(test_vector))
f3 = nmpy.dot(nmpy.transpose(percep_3.pocket_weight),nmpy.transpose(test_vector))
#Finding the maximum with dot products
var = nmpy.argmax(nmpy.array([f1, f2, f3]))
######Confusion Matrix Design##########
if(var==2 and arr[4].strip()=='Iris-setosa'):
TP += 1
TN += 1
Setosa_TP += 1
confusion[1,1] += 1
elif(var==2 and arr[4].strip()=='Iris-versicolor'):
confusion[2, 1] += 1
FP += 1
FN += 1
elif(var==2 and arr[4].strip()=='Iris-virginica'):
confusion[3, 1] += 1
FP += 1
FN += 1
elif(var==0 and arr[4].strip()=='Iris-versicolor'):
TP += 1
TN += 1
Versicolor_TP += 1
confusion[2,2] += 1
elif(var==0 and arr[4].strip()=='Iris-setosa'):
confusion[1, 2] += 1
FP += 1
FN += 1
elif(var==0 and arr[4].strip()=='Iris-virginica'):
confusion[3, 2] += 1
FP += 1
FN += 1
elif(var==1 and arr[4].strip()=='Iris-virginica'):
TP += 1
TN += 1
confusion[3,3] += 1
Virginica_TP += 1
elif(var==1 and arr[4].strip()=='Iris-versicolor'):
confusion[2, 3] += 1
FP += 1
FN += 1
elif(var==1 and arr[4].strip()=='Iris-setosa'):
confusion[1, 3] += 1
FP += 1
FN += 1
####################################################
if(var==2):
#print('Iris-setosa')
#export_data(var,line)
if(arr[4].strip()=='Iris-setosa'):
accuracy_rate += 1
elif(var==0):
#print('Iris-versicolor')
#export_data(var,line)
if(arr[4].strip()=='Iris-versicolor'):
accuracy_rate += 1
else:
#print('Iris-virginica')
#export_data(var,line)
if(arr[4].strip()=='Iris-virginica'):
accuracy_rate += 1
line = file.readline()
length += 1
#Updating the TP,FP, FN for each class
Setosa_FP = confusion[2, 1] + confusion[3, 1]
Versicolor_FP = confusion[1, 2] + confusion[3, 2]
Virginica_FP = confusion[1, 3] + confusion[2, 3]
Setosa_FN = confusion[1, 2] + confusion[1, 3]
Versicolor_FN = confusion[2, 1] + confusion[2, 3]
Virginica_FN = confusion[3, 1] + confusion[3, 2]
#Printing the confusion matrix, recall, precision for each class
print('Updated Weights')
print(percep_1.weight)
print(percep_2.weight)
print(percep_3.weight)
print('\n##########################################################################')
print('\n#### Confusion Matrix Training Data ####')
print('100 --> Predicted Setosa 101 --> Predicted Versicolor 110 --> Predicted Virginica')
print('-100 --> Real Setosa -101 --> Real Versicolor -110 --> Real Virginica')
print(confusion)
print('\nThe Accuracy is: ',accuracy_rate/length*100)
print('The Overall Matrix Accuracy is: ',(TP+TN)/(TP+TN+FP+FN)*100)
print('\nRecall (Iris_Setosa)',Setosa_TP*100/(Setosa_TP + Setosa_FN))
print('Precision (Iris_Setosa)',Setosa_TP*100/(Setosa_TP + Setosa_FP))
print('\nRecall (Iris_Versicolor)',Versicolor_TP*100/(Versicolor_TP + Versicolor_FN))
print('Precision (Iris_Versicolor)',Versicolor_TP*100/(Versicolor_TP + Versicolor_FP))
print('\nRecall (Iris_Virginica)',Virginica_TP*100/(Virginica_TP + Virginica_FN))
print('Precision (Iris_Virginica)',Virginica_TP*100/(Virginica_TP + Virginica_FP))
print('##################')
file.close()
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