def compute_correlation(x, y): stdev_x = statslib.compute_standard_deviation(len(x), x) stdev_y = statslib.compute_standard_deviation(len(y), y) if stdev_x > 0 and stdev_y > 0: covariance = compute_covariance(x, y) return covariance / stdev_x / stdev_y else: return 0
def compute_dot_product(x, y):
return sum(x_i * y_i for x_i, y_i in zip(x, y))
def compute_pearson_coefficient(x, x_mu, x_sigma, y, y_mu, y_sigma):
dot_product = compute_dot_product(x, y)
pearson_coefficient = (dot_product - n * x_mu * y_mu) / ((n - 1) * (x_sigma * y_sigma))
return pearson_coefficient
if __name__ == "__main__":
n, maths, physics, chemistry = read_input()
maths_mean = statslib.compute_mean(n, maths)
maths_sd = statslib.compute_standard_deviation(n, maths)
physics_mean = statslib.compute_mean(n, physics)
physics_sd = statslib.compute_standard_deviation(n, physics)
chemistry_mean = statslib.compute_mean(n, chemistry)
chemistry_sd = statslib.compute_standard_deviation(n, chemistry)
pearson_coefficient_maths_physics = compute_pearson_coefficient(
maths, maths_mean, maths_sd, physics, physics_mean, physics_sd
)
print "%.2f" % pearson_coefficient_maths_physics
pearson_coefficient_physics_chem = compute_pearson_coefficient(
physics, physics_mean, physics_sd, chemistry, chemistry_mean, chemistry_sd
)
