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1) SLP
#NAND
def unit(a):
if a>0:
return 1
else:
return 0

def percep(x,w,b):
v=np.dot(x,w)+b
return unit(v)

def notgate(x):
w=-1
b=1
return percep(x,w,b)

def andgate(x):
w=np.array([1,1])
b=-1
return percep(x,w,b)


def nandgate(x):
v=andgate(x)
return notgate(v)

t1=np.array([1,1])
t2=np.array([1,0])
t3=np.array([0,1])
t4=np.array([0,0])
print('NAND({},{}) = {}'.format(1,1,nandgate(t1)))
print('NAND({},{}) = {}'.format(1,0,nandgate(t2)))
print('NAND({},{}) = {}'.format(0,1,nandgate(t3)))
print('NAND({},{}) = {}'.format(0,0,nandgate(t4)))

#NOR
def unit(a):
if a>0:
return 1
else:
return 0

def percep(x,w,b):
v=np.dot(x,w)+b
return unit(v)

def ORgate(x):
w=np.array([2,2])
b=-1
return percep(x,w,b)

def notgate(x):
w=-1
b=1
return percep(x,w,b)

def norgate(x):
v=ORgate(x)
x=notgate(v)
return x


t1=np.array([1,1])
t2=np.array([1,0])
t3=np.array([0,1])
t4=np.array([0,0])
print('NOR({},{}) = {}'.format(1,1,norgate(t1)))
print('NOR({},{}) = {}'.format(1,0,norgate(t2)))
print('NOR({},{}) = {}'.format(0,1,norgate(t3)))
print('NOR({},{}) = {}'.format(0,0,norgate(t4)))

#NOT
def unit(a):
if a>0:
return 1
else:
return 0

def perceptron(x,weight,bias):
v=np.dot(x,weight)+bias
return unit(v)

def NOT_function(x):
bias=1
weight=-1
return perceptron(x,weight,bias)


test1=np.array(1)
test2=np.array(0)
print('NOT({}) = {}'.format(1,NOT_function(test1)))
print('NOT({}) = {}'.format(0,NOT_function(test2)))









2) Single Layer Perceptron for Regression
weights=[0,0]
threshold=0
leraningrate=1
bias=1
max_iterations=50
x=[
[0.65,0.42,-1],
[0.54,0.36,-1],
[0.23,0.32,1],
[0.12,0.23,1],
[0.98,0.89,-1],
[0.23,0.43,1],
]
y=0
for k in range(0,max_iterations):
hits=0
for i in range(0,len(x)):
sum=0
for j in range(0,len(x[i])-1):
sum+=weights[j]*x[i][j]
output=sum+bias
if output>threshold:
y=1
else:
y=-1
if y == x[i][2]:
hits+=1
else:
for j in range(0,len(weights)):
weights[j]+=leraningrate*x[i][j]*x[i][2]
bias+=leraningrate*x[i][2]
answer="weight updated to "+str(weights)
if y==1:
print("n"+answer)
if y==-1:
print("n"+answer)
if hits==len(x):
print("n")
print("nFunctionality learned with "+str(k)+" iterations!")
break;








3) Single Layer Perceptron for Classification
import tensorflow as tf
import matplotlib.pyplot as plt # Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1 # tf Graph Input
x = tf.placeholder("float", [none, 784]) # MNIST data image of shape 28*28 = 784 y = tf.placeholder("float", [none, 10]) # 0-9 digits recognition => 10 classes
# Create model
# Set model weights
W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10]))
# Constructing the model activation=tf.nn.softmaxx(tf.matmul (x, W)+b) # Softmax

of function
# Minimizing error using cross entropy cross_entropy = y*tf.log(activation)
cost = tf.reduce_mean (-tf.reduce_sum (cross_entropy, reduction_indice = 1)) optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Plot settings
avg_set = [] epoch_set = []
# Initializing the variables where init = tf.initialize_all_variables() # Launching the graph
with tf.Session() as sess:
sess.run(init)


# Training of the cycle in the dataset for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_example/batch_size)


# Creating loops at all the batches in the code for i in range(total_batch):


batch_xs, batch_ys = mnist.train.next_batch(batch_size)


# Fitting the training by the batch data sess.run(optimizr, feed_dict = { x: batch_xs, y: batch_ys})
# Compute all the average of loss avg_cost += sess.run(cost, feed_dict = { x: batch_xs, y: batch_ys}) //total batch
# Display the logs at each epoch steps

if epoch % display_step==0:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format (avg_cost)) avg_set.append(avg_cost) epoch_set.append(epoch+1)
print ("Training phase finished")


plt.plot(epoch_set,avg_set, 'o', label = 'Logistics Regression Training') plt.ylabel('cost')
plt.xlabel('epoch') plt.legend() plt.show()

# Test the model
correct_prediction = tf.equal (tf.argmax (activation, 1),tf.argmax(y,1))


# Calculating the accuracy of dataset
accuracy = tf.reduce_mean(tf.cast (correct_prediction, "float")) print
("Model accuracy:", accuracy.eval({x:mnist.test.images, y: mnist.test.labels}))










4) Linear regression using MLP

import pandas as pd
m=0
c=0
X=[5.1,6.2,5.8,5.5,5.0,5.3,6.0]
Y=[54,75,67,65,54,59,69]
print(list(zip(X,Y)))
data=pd.DataFrame(list(zip(X,Y)),columns=['X','Y'])
data
X=data.iloc[:,0]
Y=data.iloc[:,1]
L=0.0001
epochs=1000
n=float(len(X))
#performing gradient descent
for i in range(epochs):
Y_pred=m*X+c
D_m=(-2/n)*sum(X*(Y-Y_pred))
D_c=(-2/n)*sum(Y-Y_pred)
m=m-L*D_m#update m
c=c-L*D_c#update c
print(m,c)
def predict(x):
return m*x+c
predict(6.0)






7) ID3 Algorithm
import pandas as pd
import math
import numpy as np

data = pd.read_csv("3-dataset.csv")
features = [feat for feat in data]
features.remove("answer")

class Node:
def __init__(self):
self.children = []
self.value = ""
self.isLeaf = False
self.pred = ""

def entropy(examples):
pos = 0.0
neg = 0.0
for _, row in examples.iterrows():
if row["answer"] == "yes":
pos += 1
else:
neg += 1
if pos == 0.0 or neg == 0.0:
return 0.0
else:
p = pos / (pos + neg)
n = neg / (pos + neg)
return -(p * math.log(p, 2) + n * math.log(n, 2))

def info_gain(examples, attr):
uniq = np.unique(examples[attr])
#print ("n",uniq)
gain = entropy(examples)
#print ("n",gain)
for u in uniq:
subdata = examples[examples[attr] == u]
#print ("n",subdata)
sub_e = entropy(subdata)
gain -= (float(len(subdata)) / float(len(examples))) * sub_e
#print ("n",gain)
return gain

def ID3(examples, attrs):
root = Node()

max_gain = 0
max_feat = ""
for feature in attrs:
#print ("n",examples)
gain = info_gain(examples, feature)
if gain > max_gain:
max_gain = gain
max_feat = feature
root.value = max_feat
#print ("nMax feature attr",max_feat)
uniq = np.unique(examples[max_feat])
#print ("n",uniq)
for u in uniq:
#print ("n",u)
subdata = examples[examples[max_feat] == u]
#print ("n",subdata)
if entropy(subdata) == 0.0:
newNode = Node()
newNode.isLeaf = True
newNode.value = u
newNode.pred = np.unique(subdata["answer"])
root.children.append(newNode)
else:
dummyNode = Node()
dummyNode.value = u
new_attrs = attrs.copy()
new_attrs.remove(max_feat)
child = ID3(subdata, new_attrs)
dummyNode.children.append(child)
root.children.append(dummyNode)
return root

def printTree(root: Node, depth=0):
for i in range(depth):
print("t", end="")
print(root.value, end="")
if root.isLeaf:
print(" -> ", root.pred)
print()
for child in root.children:
printTree(child, depth + 1)

root = ID3(data, features)
printTree(root)









8) Naive Baye’s Algorithm
from sklearn.datasets import load_iris
iris=load_iris()

X=iris.data
y=iris.target

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4,random_state=1)

from sklearn.naive_bayes import GaussianNB
gnb=GaussianNB()
gnb.fit(X_train,y_train)
y_pred=gnb.predict(X_test)

from sklearn import metrics
print("Gaussian Naive Bayes model accuracy(in %):",metrics.accuracy_score(y_test,y_pred)*100)



9) Principal Component Analysis
import numpy as np
from numpy import array
from numpy import mean
from numpy import cov
from numpy.linalg import eig

A=array([[2,3],[5,6],[8,9]])
print(A)
M=mean(A.T,axis=1)
C=A-M
print(M)
print(C)
V=cov(C.T)
print(V)
values,vectors=eig(V)
print(vectors)
print(values)
P=vectors.T.dot(C.T)
print(P.T)
print(cov(P))








10) Gray-Level Co-Occurrence Matrix (GLCM)
from skimage.feature import greycomatrix
import numpy as np
image = np.array([[0, 0, 1, 1],
[0, 0, 1, 1],
[0, 2, 2, 2],
[2, 2, 3, 3]], dtype=np.uint8)

result = greycomatrix(image, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], levels =4, symmetric=True, normed=True)#call function
print(result)







11) Clustering Algorithm
from sklearn.cluster import KMeans
import numpy as np
X=np.array([[1,2],[1,4],[2,0],[10,4],[11,2],[8,6]])
kmeans=KMeans(n_clusters=2,random_state=1)
kmeans.fit(X)
print(kmeans.labels_)
print(kmeans.predict([[1,2]]))
print(kmeans.predict([[10,1]]))
print(kmeans.cluster_centers_)









12) Support Vector Machines
from sklearn.datasets import load_iris
iris=load_iris()
X=iris.data
y=iris.target
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4,random_state=1)
model=SVC(kernel='linear')
model.fit(X_train,y_train)
y_pred=model.predict(X_test)
from sklearn.metrics import accuracy_score
print('accuracy- ',accuracy_score(y_test,y_pred))









13) Building A Simple Neural Network

from sklearn.datasets import load_iris
iris=load_iris()
x=iris.data
y=iris.target
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))
rescaledx=scaler.fit_transform(x)
from keras.models import Sequential
from keras.layers import Dense
model=Sequential()
model.add(Dense(8,input_dim=4,activation='relu'))
model.add(Dense(7,activation='relu'))
model.add(Dense(7,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(rescaledx,y,epochs=100,batch_size=10)
_,accuracy=model.evaluate(rescaledx,y)
print(accuracy)






14) Importance of Data Pre-Processing in Machine Learning
import pandas as pd
import scipy
import numpy as np
from sklearn.preprocessing import MinMaxScaler
names=['preg','plas','pres','skin','test','mass','pedi','age','class']
data=pd.read_csv('pima-indians-diabetes.data.csv',header=None,names=names)
array=data.values
X=array[:,0:8]
Y=array[:,8]
scaler=MinMaxScaler(feature_range=(0,1))
rescaledX=scaler.fit_transform(X)np.set_printoptions(precision=3)
print(rescaledX[0:10,:])
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(12,input_dim=8,activation='relu'))
model.add(Dense(8,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(X,Y,epochs=200,batch_size=10)
loss,accuracy=model.evaluate(X,Y)
print('Accuracy: %.2f' % (accuracy*100))
from numpy import array
Xp = array([[6.0, 158.0, 72.0, 35.0, 0.0, 55.5, 0.627, 50.0]])
Yp = model.predict(Xp)
print(Xp[0],Yp[0])










15) Loading Dataset in Machine Learning
1)
import csv
import numpy as np path=r"/content/diabetes (1).csv"
with open(path,'r') as f:
reader = csv.reader(f,delimiter = ",")
headers = next(reader)
data = list(reader)
data = np.array(data).astype(float)
data
2)
from numpy import loadtxt
from numpy import genfromtxt
path = r"/content/Iris.csv"
datapath= open(path,'r')
data = genfromtxt(datapath)
print(data.shape)
print(data[:3])
3)
from pandas import read_csv
path = r"C:pima-indians-diabetes.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(path, names=headernames)
print(data.shape)
print(data[:3])





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