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Update classification.py #1

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84 changes: 32 additions & 52 deletions classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,49 +5,32 @@
############### data ###############
path = "C:\\ML\\Classification\\First_Project_Classification\\data.txt"
data = pd.read_csv(path,header= None,names=["Exam1","Exam2","Admitted"])
# print(data)

data.insert(0,"Bais",1)
# print(data)

# separate featurs and output
col =data.shape[1] # output number of columns for data
x = data.iloc[ : , 0 : col - 1]
y = data.iloc[ : , col - 1 : col]

# print("x\n",x)
# print("="*50)
# print("y\n",y)

# separate positive and negative data
positive = data[data["Admitted"].isin([1])]
negative = data[data["Admitted"].isin([0])]

# print(positive)
# print(negative)

# draw positive and negative data
# fig,ax = plt.subplots()
# ax.scatter(positive["Exam1"],positive["Exam2"],marker='o',c='b')

# ax.scatter(negative["Exam1"],negative["Exam2"],marker='x',c='r')
# plt.show()
fig,ax = plt.subplots()
ax.scatter(positive["Exam1"],positive["Exam2"],marker='o',c='b')
ax.scatter(negative["Exam1"],negative["Exam2"],marker='x',c='r')
plt.show()

# convert x , y to matrix
x = np.matrix(x.values)
y= np.matrix(y.values)
theta = np.zeros((x.shape[1],1))

# print(x)
# print(f"y shape : {y.shape}")
# print(f"x shape : {x.shape}")
# print(f"theta shape : {theta.shape}")

# implement Sigmoid Function
def Sigmoid(z):
return 1 / (1 + np.exp(-z))
# test = np.log(Sigmoid(x @ theta))
# print (f"test {test.shape}")

#implement Cost Function
def Cost(theta,x, y):
Expand All @@ -67,27 +50,6 @@ def Gradient(theta,x,y):
grad = (1/m) * (x.T @ error)
return grad

result = sc.optimize.fmin_tnc(func=Cost, x0=theta, args=(x, y), fprime=Gradient)


newTheta = result[0]

print(f"newtheta : {newTheta.shape}")
print(f"x : {x.shape}")
print(f"y : {y.shape}")



# print(f"newTheta :\n{newTheta}")
# print("============================")
cost0 = Cost(theta,x,y)

print(f"Cost Before = {cost0}")
print("============================")
cost1 = Cost(newTheta,x,y)

print(f"Cost After: {cost1}")

def predict(x, theta):
p = Sigmoid(x @ newTheta)
for i in range(96):
Expand All @@ -97,19 +59,37 @@ def predict(x, theta):
p[0,i] = 0
return p

t = predict(x,newTheta)
""""""""" Training Data """""""""

print(t.reshape(96,1))
result = sc.optimize.fmin_tnc(func=Cost, x0=theta, args=(x, y), fprime=Gradient)

# p = Sigmoid(x @ newTheta)

# print(p.shape)
newTheta = result[0]

# for i in range(96):
# if p[0,i] > 0.5:
# p[0,i] = 1
# else:
# p[0,i] = 0
print(f"newtheta : {newTheta.shape}")
print(f"x : {x.shape}")
print(f"y : {y.shape}")

cost0 = Cost(theta,x,y)
print(f"Cost Before = {cost0}")
print("============================")

# print(p)
cost1 = Cost(newTheta,x,y)
print(f"Cost After: {cost1}")

prediction_values = predict(x,newTheta)
prediction_values = prediction_values.reshape(96,1)

print(prediction_values.shape)
print(y.shape)
compare = np.zeros((96, 2))
compare[:, 0] = prediction_values.flatten()
compare[:, 1] = y.flatten()
print(compare)
a= 0
for i in range(96):
if compare[i,0] == compare[i,1]:
a += 1
accurecy = (a/96) * 100
print(f"Accurecy : {accurecy}%")
print("============================")