from numpy import corrcoef
from functools import reduce

f_vals = [1, 2, 3, 4]
g_vals = [1, 2, 10, 7]

num_r = corrcoef(f_vals, g_vals)
print(num_r)

print(num_r[0][1])

# here is the program using the formula
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient

f_mean = reduce(lambda x,y: x+y, f_vals)/len(f_vals)
g_mean = reduce(lambda x,y: x+y, g_vals)/len(f_vals)

f_v_m = list(map(lambda x: x-f_mean, f_vals))
print(f_v_m)
g_v_m = list(map(lambda x: x-g_mean, g_vals))
print(g_v_m)

fg_zip = list(zip(f_v_m, g_v_m))
print(fg_zip)
fg_v_m = list(map(lambda x: x[0]*x[1], fg_zip))
print(fg_v_m)

numer = reduce(lambda x,y: x+y,
            map(lambda x,y: x*y, f_v_m, g_v_m))

f_v_m_sq = list(map(lambda x: x**2, f_v_m))
print(f_v_m_sq)
g_v_m_sq = list(map(lambda x: x**2, g_v_m))
print(g_v_m_sq)

denom = reduce(lambda x,y: x+y, f_v_m_sq) * reduce(lambda x,y: x+y, g_v_m_sq)

corr = numer/(denom**(1/2))
print(corr)