Codebook, wrong output in shape of an array

[Adin]

Hi, I have the following error in Codebook Codercise F.1.4:
Error: operands could not be broadcast together with shapes (8,) (4,)

I have run it in my jupyter notebook and I have shape (8,)
Best,
Adin

I am running this code

def fft_multiplication(poly_a, poly_b):
“”"Returns the result of multiplying two polynomials

Args:
    poly_a (array[complex]): 1-D array of coefficients 
    poly_b (array[complex]): 1-D array of coefficients 

Returns: 
    array[complex]: complex coefficients of the product
        of the polynomials
"""
##################
# YOUR CODE HERE #
################## 

# Calculate the number of values required
value= len(poly_a)+ len(poly_b)+ 1
# Figure out the nearest power of 2
n= nearest_power_of_2(value)
# Pad zeros to the polynomial
poly_a= np.pad(poly_a, (0,n-len(poly_a)))
poly_b= np.pad(poly_b, (0,n-len(poly_b)))
# Convert the polynomials to value representation 
value_a= coefficients_to_values(poly_a)
value_b= coefficients_to_values(poly_b)
# Multiply
#value_c= np.zeros((n,),dtype=complex)
#for index in range(n):
#    value_c[index]= value_a[index] * value_b[index]
value_c= np.multiply(value_a,value_b)
#print(value_c)
# Convert back to coefficient representation
return values_to_coefficients(value_c)

[Adin]

This post helped me

F.1 Stuck on function

The error is still with the calculation for the degree of the resulting polynomial! Let’s just take a step back and work out some things mathematically. Let’s say I have two polynomials: f(x) = a + bx + cx^2, and g(x) = \alpha + \beta x, where a, b, c, \alpha, \beta are all real numbers for the sake of the argument. If we’re coding this in the context of this problem in the codebook, len(f) = 3 len(g) = 2 Let’s multiply f and g: \begin{align*} h(x) = f(x) \times g(x) & = \alpha (a + b…

[Alvaro_Ballon]

Glad to know you figured it out! Yes, it’s a bit sneaky

[MohammadReza_Moradi]

##################
# YOUR CODE HERE #
################## 

# Calculate the number of values required
n=max(len(poly_a), len(poly_b))+1
# Figure out the nearest power of 2
np2=nearest_power_of_2(n)

# Pad zeros to the polynomial
a= np.pad(poly_a, (0, np2-len(poly_a)), 'constant')
b= np.pad(poly_b, (0, np2-len(poly_b)), 'constant')
# Convert the polynomials to value representation 
c=coefficients_to_values(a)
d=coefficients_to_values(b)


# Multiply
e=np.multiply(value_a,value_b)

# Convert back to coefficient representation

return values_to_coefficients(e)