Shapes not aligned: Scipy's fmin_tnc function

Question

I am trying to solve on my own the first part of the second exercise (logistic regression): https://github.com/jdwittenauer/ipython-notebooks.

I am stuck when calculating the result variable. This is the last part of the error message:

File "<ipython-input-51-39288db7a045>", line 55, in ComputeCost
    h=sigmoid(np.dot(X,theta))

  File "<__array_function__ internals>", line 6, in dot

ValueError: shapes (3,) and (100,1) not aligned: 3 (dim 0) != 100 (dim 0)

Here's a piece of my code (sorry for my poor programming skills, I am a beginner):

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy.optimize as opt
data=pd.read_csv('ex2data1.txt', names=['exam1','exam2','admitted'])
def sigmoid(z):
    g=1/(1+np.exp(-z))
    return g
def whichisy(data, col):
    if not isinstance(data, pd.DataFrame):
        raise ValueError('Data is not a pandas DataFrame')
    y=data.iloc[:,col]
    X=data.drop(columns=data.columns[col])
    X.insert(0,'Ones', 1)
    X=np.array(X)
    y=np.array(y)
    y=np.reshape(y, (len(y),1))
    return X,y
X,y=whichisy(data,2)

def ComputeCost(X, y, theta):
    h=sigmoid(np.dot(X,theta))
    cost=1/len(y)*np.sum(-y*np.log(h)-(1-y)*np.log(1-h))
    return cost

def Gradient(X, y, theta):
    g=np.zeros(len(theta))
    prodotto=sigmoid(np.dot(X,theta))-y
    for k in range(len(theta)):
        g[k]=1/len(y)*np.sum(prodotto*np.reshape(X[:,k],(len(X),1)))
    return g

theta=np.zeros([np.shape(X)[1],1])
result=opt.fmin_tnc(ComputeCost, theta, fprime=Gradient, args=(X,y))

I saw and understood the code in the solutions of this exercise, but I would like to solve it using my version, but I don't get where I'm wrong.

I have unsuccessfully tried to flatten the returned value of my gradient function (as suggested here matrices are not aligned Error: Python SciPy fmin_bfgs) and the 'y' array (How to get dimensions right using fmin_cg in scipy.optimize).

Thanks!

Edit 1: After placing theta as the first argument when defining ComputeCost and Gradient functions, the fmin_tnc function runs but it fails the minimization.

result=opt.fmin_tnc(ComputeCost, theta, Gradient, args=(X,y))

And returns: (array([4.60628149e-05, 5.53178320e-03, 5.18798215e-03]), 45, 4). So rc (Return Code) is not 1 as it should be.

I've also tried to run result=opt.minimize(ComputeCost, theta, method='TNC', jac=Gradient, args=(X,y)) to check if the problem were in the fmin_tnc function, but the returned value is the same.

Answer 1

try move 'theta' ahead of 'X' and 'y' in def of ComputeCost and Gradient e.g.

def ComputeCost(theta, X, y):
    # adjust the parameters in your code accordingly
    ...