How to use for loop with dictionary and a array at the same time

Question

i want to make a linear equation with some dynamic inputs like it can be

y = θ0*x0 + θ1*x1

or

y = θ0*x0 + θ1*x1 + θ2*x2 + θ3*x3 + θ4*x4

for that i have dictionary for x0,x1,x2......xn and array for θ0,θ1,θ2......θn

im new to python so i tried this function but im stuck

so my question is how can i write a fucntion that gets x_values and theta_values as parameters and gives y_values as output

X = pd.DataFrame({'x0': np.ones(6), 'x1': np.linspace(0, 5, 6)})
θ = np.matrix('0 1')

def line_func(features, parameters):
    result = []
    for feat, param in zip(features.iteritems(), parameters):
        for i in feat:
            result.append(i*param)
    return result

line_func(X,θ)

Hi, welcome to SO. One question, why do you want to make one side a dictionary? If your variables are numbered anyways, you could also access the second structure by integer index and store the values as a list?! — jottbe
– jottbe, Commented Aug 14, 2019 at 17:18
I ask because then iterating over it would be much easier, you could just use for theta, x in zip(thetalist, xlist): ... — jottbe
– jottbe, Commented Aug 14, 2019 at 17:19
@jottbe thank you for answering.there is so many things in my code that i dont know how it actually works so thats why i can't fix it properly how can i use zip with X and theta together because they have different lenghts like theta only has 2 numbers but X can be 500+ — mert kaan
– mert kaan, Commented Aug 14, 2019 at 17:29
If you would want to work with pandas the whole way down (which I wouldn't recommend), I guess the right way would be to multiply the "column-series" of your dataframe with the corresponding theta value. I'l add it below. — jottbe
– jottbe, Commented Aug 14, 2019 at 17:43

jottbe · Accepted Answer · 2019-08-14 17:57:56Z

If you want to multiply your thetas with a list of features, then you technically mulitply a matrix (the features) with a vector (theta).

You can do this as follows:

import numpy as np

x_array= x.values
theta= np.array([theta_0, theta_1])
x_array.dot(theta)

Just order your theta-vector the way your columns are ordered in x. But note, that this gives a row-wise sum of the products for theta_i*x_i for all is. If you don't want it to be summed up rowise, you just need to write x_array * theta.

If you want to work with pandas (which I wouldn't recommend) also for the mulitplication and want to get a dataframe with the products of the column value and the corresponding theta, you could do this as follows:

# define the theta-x mapping (theta-value per column name in x)
thetas={'x1': 1, 'x2': 3}

# create an empty result dataframe with the index of x
df_result= pd.DataFrame(index=x.index)

# assign the calculated columns in a loop
for col_name, col_series in x.iteritems():
    df_result[col_name]= col_series*thetas[col_name] 

df_result

This results in:

   x1  x2
0   1   6
1  -1   3

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