CODEWITHSUNDEEP
pythonpython-3.xnumpy
some_wild_one
some_wild_one

Reputation: 15

mathematical formula in python doesnt work

I have been learning machine learning in python and currently im studying the basics. Im now studying linear regression and trying to implement some mathematical formulas into python code. I managed to write some formulas successfuly but there is this one im having a hard time with: (X - Xmeans) * (Y - Ymeans) It always gives me an error of "list indices must be integers or slices, not numpy.float64" when I try to print it.

I've tried to look for same cases and their solutions in the web but none of them worked.


import numpy
import matplotlib.pyplot as plt

X_positions = numpy.array([2,3,4,5,6])
y_positions = numpy.array([4,5,6,5,7])
plt.plot([X_positions], [y_positions], 'ro')
plt.axis([0,10,0,10])

X_means = sum(X_positions) / len(X_positions)
y_means = sum(y_positions) / len(y_positions)

plt.plot([X_means], [y_means], 'go')
plt.axis([0,10,0,10])

X_minus_X_means = []
y_minus_y_means = []
X_minus_X_means_squared = []

for i in X_positions:
    X_minus_X_means.append(i - X_means)

for i in y_positions:
    y_minus_y_means.append(i - y_means)

for i in X_minus_X_means:
    X_minus_X_means_squared.append(i ** 2)

X_minus_X_means_times_y_minus_y_means = []

#HERE IS THE PROBLEM
for i in X_minus_X_means and y_minus_y_means:
   X_minus_X_means_times_y_minus_y_means.append(X_minus_X_means[i] * y_minus_y_means[i])

Upvotes: 1

Views: 129

Answers (5)

Sheldore
Sheldore

Reputation: 39072

While I completely support and prefer vectorized operations, I think you should know about the function called zip which is helpful in situations where you are iterating over two (or more) lists in parallel. So in your case, the problematic part should be changed to the following avoiding any index i

for x, y in zip(X_minus_X_means, y_minus_y_means):
    X_minus_X_means_times_y_minus_y_means.append(x * y)

Upvotes: 0

AviatorX
AviatorX

Reputation: 532

both X_minus_X_means and y_minus_y_means are list

y_minus_y_means contain value 

[-1.4000000000000004,
 -0.40000000000000036,
 0.5999999999999996,
 -0.40000000000000036,
 1.5999999999999996]

so basically inside for i in X_minus_X_means and y_minus_y_means: value of i calculated in operation is of type numpy.float64 and inside loop, you are accessing value of X_minus_X_means and y_minus_y_means using index which is i a float value

Upvotes: 0

Grant Williams
Grant Williams

Reputation: 1537

You should really just use the built-in numpy operations and the vectorized operations when possible.

Try something like this:

import numpy as np

import matplotlib.pyplot as plt

X_positions = np.array([2,3,4,5,6])
y_positions = np.array([4,5,6,5,7])
plt.plot([X_positions], [y_positions], 'ro')
plt.axis([0,10,0,10])

X_means = X_positions.mean()
y_means = y_positions.mean()

plt.plot([X_means], [y_means], 'go')
plt.axis([0,10,0,10])

X_minus_X_means = X_positions-X_means
y_minus_y_means = y_positions-y_means
X_minus_X_means_squared = X_minus_X_means**2
X_minus_X_means_times_y_minus_y_means = X_minus_X_means*y_minus_y_means

Upvotes: 3

3sm1r
3sm1r

Reputation: 530

Instead of 

for i in X_minus_X_means and y_minus_y_means:

try to write

for i in range(len(X_minus_X_means)):

otherwise i is not an integer and cannot be used as an index

Upvotes: 1

jspcal
jspcal

Reputation: 51934

Perhaps the division is yielding a float which is causing the index to be a non-integer?

X_means = sum(X_positions) / len(X_positions)

For integer division in python3, the double slash operator // is available:

X_means = sum(X_positions) // len(X_positions)

You could also use ceil, floor, round, or int(val).

Upvotes: 0

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