How To Hack NASA by HTML in steps

import csv

with open('/content/drive/MyDrive/ML lab programs/Lab 1 Find-S algorithm/weather.csv') as f:

  reader = csv.reader(f)

  data = list(reader) 

print('Training data')

for row in data:

  print(row)

attr_len=len(data[0])-1

h = ['0']*attr_len      

print('The Hypothesis are')

for row in data:

  if row[-1] == 'Yes':

    j = 0

    for col in row: 

      if col != 'Yes': 

        if col != h[j] and h[j] == '0':

          h[j] = col

        elif col != h[j] and h[j] != '0':

          h[j] = '?'

      j = j + 1

  print(h)

print('Maximally Specific Hypothesis:', h)

2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

import csv

with open('/content/drive/MyDrive/ML lab programs/Lab 2 Candidate Selection Algorithm/enjoysport1.csv') as f:

  reader = csv.reader(f)

  data = list(reader) 

print('Training data')

for row in data:

  print(row)

print('-------------------------------------------------------------')

attr_len=len(data[0])-1

s = ['0']*attr_len      

g = ['?']*attr_len      

temp=[] 

print('the Hypothesis are')

print('S=',s)

print('G=',g)

print('-------------------------------------------------------------')

for row in data:

  if row[-1] == 'yes':

    j=0

    for col in row:

      if col != 'yes':

        if col != s[j] and s[j] == '0':

          s[j] = col

        elif col != s[j] and s[j] != '0':

          s[j] = '?'

      j+= 1

    for j in range(0,attr_len):

      for k in temp:

        if k[j] != s[j] and k[j] != '?':

          temp.remove(k)

  elif row[-1] == 'no':

    j = 0

    for col in row:

      if col != 'no':

        if col != s[j] and s[j] != '?':

          g[j] = s[j]

          temp.append(g)

          g = ['?'] * attr_len

      j+=1

  print('S=',s)

  if len(temp) == 0:

    print('G=',g)

  else:

    print('G=',temp)

  print('-------------------------------------------------------------')

4444444444444444444/////////////////////////////////////

import numpy as np

X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)

y = np.array(([92], [86], [89]), dtype=float)

X = X/np.amax(X,axis=0) 

y = y/100

def sigmoid (x):

    return 1/(1 + np.exp(-x))

def derivatives_sigmoid(x):

    return x * (1 - x)

epoch=5 

lr=0.1 

inputlayer_neurons = 2 

hiddenlayer_neurons = 3 

output_neurons = 1 

wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))

bh=np.random.uniform(size=(1,hiddenlayer_neurons))

wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))

bout=np.random.uniform(size=(1,output_neurons))

for i in range(epoch):

    

    hinp1=np.dot(X,wh)

    hinp=hinp1 + bh

    hlayer_act = sigmoid(hinp)

    outinp1=np.dot(hlayer_act,wout)

    outinp= outinp1+bout

    output = sigmoid(outinp)

    

    

    EO = y-output

    outgrad = derivatives_sigmoid(output)

    d_output = EO * outgrad

    EH = d_output.dot(wout.T)

    hiddengrad = derivatives_sigmoid(hlayer_act)

    d_hiddenlayer = EH * hiddengrad

    

    wout += hlayer_act.T.dot(d_output) *lr   

    bout += np.sum(d_output, axis=0,keepdims=True) *lr

    wh += X.T.dot(d_hiddenlayer) *lr

    bh += np.sum(d_hiddenlayer, axis=0,keepdims=True) *lr

print("Input: \n" ,X) 

print("Actual Output: \n" ,y)

print("Predicted Output: \n" ,output)

6////////////////////////////////////////////

import pandas as pd 

msg=pd.read_csv('/content/drive/MyDrive/ML lab programs/lab 6 bayesian classifier/data6.csv',names=['message','label']) #Tabular form data 

print('Total instances in the dataset:',msg.shape[0])

msg['labelnum']=msg.label.map({'pos':1,'neg':0}) 

X=msg.message

Y=msg.labelnum

from sklearn.model_selection import train_test_split 

xtrain,xtest,ytrain,ytest=train_test_split(X,Y) 

print('\nDataset is split into Training and Testing samples') 

print('Total training instances :', ytrain.shape[0]) 

print('Total testing instances :', ytest.shape[0])

from sklearn.feature_extraction.text import CountVectorizer 

count_vect = CountVectorizer()

xtrain_dtm = count_vect.fit_transform(xtrain) #Sparse matrix 

xtest_dtm = count_vect.transform(xtest)

print('\nTotal features extracted using CountVectorizer:',xtrain_dtm.shape[1])

print('\nThe words or Tokens in the text documents\n') 

print(count_vect.get_feature_names())

from sklearn.naive_bayes import MultinomialNB

clf = MultinomialNB().fit(xtrain_dtm,ytrain) 

predicted = clf.predict(xtest_dtm)

from sklearn import metrics 

print('\nAccuracy metrics')

print('==================')

print('Accuracy of the classifer is',metrics.accuracy_score(ytest,predicted))

print('Recall :',metrics.recall_score(ytest,predicted), '\nPrecison :',metrics.precision_score(ytest,predicted))

print('Confusion matrix')

print('==================')

print(metrics.confusion_matrix(ytest,predicted))

7.............................................................

import pandas as pd

import numpy as np

data = pd.read_csv('/content/drive/MyDrive/ML lab programs/lab 7 bayesian network/heart.csv')

data = data.replace('?',np.nan)

#display the data

print('Sample instances from the dataset are given below')

print(data.head())

#display the Attributes names and datatyes

print('\n Attributes and datatypes')

print(data.dtypes)

from pgmpy.models import BayesianModel

from pgmpy.estimators import MaximumLikelihoodEstimator

from pgmpy.inference import VariableElimination

model =BayesianModel([('age','heartdisease'),('sex','heartdisease'),

                      ('exang','heartdisease'),('cp','heartdisease'),

                      ('heartdisease','restecg'),('heartdisease','chol')])

import networkx as nx

import matplotlib.pyplot as plt

nx.draw(model, with_labels = True); 

plt.show()

print('\n Learning CPD using Maximum likelihood estimators')

model.fit(data,estimator=MaximumLikelihoodEstimator)

print('\n Inferencing with Bayesian Network:')

infer = VariableElimination(model)

print('\n 1.Probability of HeartDisease given evidence=restecg :1')

q1=infer.query(variables=['heartdisease'],evidence={'restecg':1})

print(q1)

print('\n 2.Probability of HeartDisease given evidence= cp:2 ')

q2=infer.query(variables=['heartdisease'],evidence={'cp':2})

print(q2)

8.....................................................

import matplotlib.pyplot as plt

from sklearn import datasets

import sklearn.metrics as sm

import pandas as pd

import numpy as np

iris = datasets.load_iris()

X = pd.DataFrame(iris.data)

X.columns = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width']

y = pd.DataFrame(iris.target)

y.columns = ['Targets']

plt.figure(figsize=(14,7))

colormap = np.array(['red', 'lime', 'black'])

plt.subplot(1, 3, 1)

plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[y.Targets], s=40)

plt.title('Real Classification')

plt.xlabel('Petal Length')

plt.ylabel('Petal Width')

from sklearn.cluster import KMeans

model = KMeans(n_clusters=3)

model.fit(X)

plt.subplot(1, 3, 2)

plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[model.labels_], s=40)

plt.title('K Mean Classification')

plt.xlabel('Petal Length')

plt.ylabel('Petal Width')

print('The accuracy score of K-Mean: ',sm.accuracy_score(y, model.labels_))

print('The Confusion matrixof K-Mean: ',sm.confusion_matrix(y, model.labels_))

from sklearn.mixture import GaussianMixture

gmm = GaussianMixture(n_components=3)

gmm.fit(X)

y_gmm = gmm.predict(X)

plt.subplot(1, 3, 3)

plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[y_gmm], s=40)

plt.title('GMM Classification')

plt.xlabel('Petal Length')

plt.ylabel('Petal Width')

print('The accuracy score of EM: ',sm.accuracy_score(y, y_gmm))

print('The Confusion matrix of EM: ',sm.confusion_matrix(y, y_gmm))