Classification algorithm [on hold]

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I just wrote a classification algorithm and would like to get some feedback if possible. Is this the right way to implement this code or it should be done any other way?

import numpy as np

import math

import matplotlib.pyplot as plt



data = np.array([[1,0],[2,0],[3,0],[4,1],[5,1],[6,1],[7,1]])



#Adding a column of ones to our x matrix

ones = np.ones((len(data),1))

x = np.concatenate((ones, data[:,0].reshape(-1,1)), axis=1)



#Making our y vector

y = data[:,1].reshape(-1,1)



#choosing random Theta

theta = np.zeros((2,1))



#Sigmoid Function



def g(n):

    return 1 / ((1 + (math.e**(-n))) + 0.000000001)

#Settings

m = 1/(len(x))

alpha = 2

iteration = 1000

while iteration > 0:

    iteration -= 1

    z = x @ theta

    #cost function

    h = g(z).reshape((-1,1))

    part1 = -y.T @ np.log(h)

    part2 = ((1 - y).T) @ (np.log(1-h))

    j = m * (part1 - part2)

    print(j)

    #Gradient descent

    inner = h - y

    outer = x.T @ inner

    dj = (alpha/m) * outer

    theta = theta - dj

print(theta)

edited yesterday

Jamal♦

30.2k11115226

asked yesterday

wassim khadim

New contributor

put on hold as unclear what you're asking by Graipher, Toby Speight, Sᴀᴍ Onᴇᴌᴀ, t3chb0t, Edward 5 hours ago

Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

1

Could you tell us something more about your code? It could be an interesting question but without any description it's likely to get closed.
– t3chb0t
11 hours ago

add a comment |

up vote
-1
down vote

favorite

I just wrote a classification algorithm and would like to get some feedback if possible. Is this the right way to implement this code or it should be done any other way?

import numpy as np

import math

import matplotlib.pyplot as plt



data = np.array([[1,0],[2,0],[3,0],[4,1],[5,1],[6,1],[7,1]])



#Adding a column of ones to our x matrix

ones = np.ones((len(data),1))

x = np.concatenate((ones, data[:,0].reshape(-1,1)), axis=1)



#Making our y vector

y = data[:,1].reshape(-1,1)



#choosing random Theta

theta = np.zeros((2,1))



#Sigmoid Function



def g(n):

    return 1 / ((1 + (math.e**(-n))) + 0.000000001)

#Settings

m = 1/(len(x))

alpha = 2

iteration = 1000

while iteration > 0:

    iteration -= 1

    z = x @ theta

    #cost function

    h = g(z).reshape((-1,1))

    part1 = -y.T @ np.log(h)

    part2 = ((1 - y).T) @ (np.log(1-h))

    j = m * (part1 - part2)

    print(j)

    #Gradient descent

    inner = h - y

    outer = x.T @ inner

    dj = (alpha/m) * outer

    theta = theta - dj

print(theta)

edited yesterday

Jamal♦

30.2k11115226

asked yesterday

wassim khadim

New contributor

put on hold as unclear what you're asking by Graipher, Toby Speight, Sᴀᴍ Onᴇᴌᴀ, t3chb0t, Edward 5 hours ago

1

Could you tell us something more about your code? It could be an interesting question but without any description it's likely to get closed.
– t3chb0t
11 hours ago

add a comment |

up vote
-1
down vote

favorite

I just wrote a classification algorithm and would like to get some feedback if possible. Is this the right way to implement this code or it should be done any other way?

import numpy as np

import math

import matplotlib.pyplot as plt



data = np.array([[1,0],[2,0],[3,0],[4,1],[5,1],[6,1],[7,1]])



#Adding a column of ones to our x matrix

ones = np.ones((len(data),1))

x = np.concatenate((ones, data[:,0].reshape(-1,1)), axis=1)



#Making our y vector

y = data[:,1].reshape(-1,1)



#choosing random Theta

theta = np.zeros((2,1))



#Sigmoid Function



def g(n):

    return 1 / ((1 + (math.e**(-n))) + 0.000000001)

#Settings

m = 1/(len(x))

alpha = 2

iteration = 1000

while iteration > 0:

    iteration -= 1

    z = x @ theta

    #cost function

    h = g(z).reshape((-1,1))

    part1 = -y.T @ np.log(h)

    part2 = ((1 - y).T) @ (np.log(1-h))

    j = m * (part1 - part2)

    print(j)

    #Gradient descent

    inner = h - y

    outer = x.T @ inner

    dj = (alpha/m) * outer

    theta = theta - dj

print(theta)

edited yesterday

Jamal♦

30.2k11115226

asked yesterday

wassim khadim

New contributor

I just wrote a classification algorithm and would like to get some feedback if possible. Is this the right way to implement this code or it should be done any other way?

import numpy as np

import math

import matplotlib.pyplot as plt



data = np.array([[1,0],[2,0],[3,0],[4,1],[5,1],[6,1],[7,1]])



#Adding a column of ones to our x matrix

ones = np.ones((len(data),1))

x = np.concatenate((ones, data[:,0].reshape(-1,1)), axis=1)



#Making our y vector

y = data[:,1].reshape(-1,1)



#choosing random Theta

theta = np.zeros((2,1))



#Sigmoid Function



def g(n):

    return 1 / ((1 + (math.e**(-n))) + 0.000000001)

#Settings

m = 1/(len(x))

alpha = 2

iteration = 1000

while iteration > 0:

    iteration -= 1

    z = x @ theta

    #cost function

    h = g(z).reshape((-1,1))

    part1 = -y.T @ np.log(h)

    part2 = ((1 - y).T) @ (np.log(1-h))

    j = m * (part1 - part2)

    print(j)

    #Gradient descent

    inner = h - y

    outer = x.T @ inner

    dj = (alpha/m) * outer

    theta = theta - dj

print(theta)

python python-3.x machine-learning

edited yesterday

Jamal♦

30.2k11115226

asked yesterday

wassim khadim

New contributor

edited yesterday

Jamal♦

30.2k11115226

asked yesterday

wassim khadim

New contributor

edited yesterday

Jamal♦

30.2k11115226

edited yesterday

Jamal♦

30.2k11115226

edited yesterday

Jamal♦

30.2k11115226

asked yesterday

wassim khadim

New contributor

asked yesterday

wassim khadim

asked yesterday

wassim khadim

New contributor

wassim khadim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

put on hold as unclear what you're asking by Graipher, Toby Speight, Sᴀᴍ Onᴇᴌᴀ, t3chb0t, Edward 5 hours ago

1

Could you tell us something more about your code? It could be an interesting question but without any description it's likely to get closed.
– t3chb0t
11 hours ago

add a comment |

1

Could you tell us something more about your code? It could be an interesting question but without any description it's likely to get closed.
– t3chb0t
11 hours ago

Could you tell us something more about your code? It could be an interesting question but without any description it's likely to get closed.
– t3chb0t
11 hours ago

add a comment |

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