chapter_1.py

import numpy as np


def w_sum(a, b):
    assert len(a) == len(b)
    output = 0
    for i in range(len(a)):
        output += a[i] * b[i]
    return output


# weights = [0.1, 0.2, 0]
# toes %win #fans
ih_wgt = np.array(
    [[0.1, 0.2, -0.1], [-0.1, 0.1, 0.9], [0.1, 0.4, 0.1]]  # hid[0]  # hid[1]
)  # hid[2]

# hid[0] hid[1] hid[2]
hp_wgt = np.array([[0.3, 1.1, -0.3], [0.1, 0.2, 0.0], [0.0, 1.3, 0.1]])  # hurt?  # win?  # sad?

weights = [ih_wgt, hp_wgt]


def vect_mat_mul(vect, matrix):
    assert len(vect) == len(matrix)
    output = [0, 0, 0]
    for i in range(len(vect)):
        output[i] = w_sum(vect, matrix[i])
    return output


def neural_network(input, weights):
    hid = input.dot(weights[0])
    pred = hid.dot(weights[1])
    return pred


toes = np.array([8.5, 9.5, 9.9, 9.0])
wlrec = np.array([0.65, 0.8, 0.8, 0.9])
nfans = np.array([1.2, 1.3, 0.5, 1.0])

# Input corresponds to every entry
# for the first game of the season.

input = np.array([toes[0], wlrec[0], nfans[0]])
pred = neural_network(input, weights)

print(pred)

"""
def average(input):
    summ = sum(input)
    return summ / len(input)
print(average(toes))
"""

# print(neural_network(np.array([toes[0], wlrec[0], nfans[0]]), np.array([0.1, 0.2, -0.1])))
"""
w1 = [toes[0], wlrec[0], nfans[0]]
w2 = [0.1, 0.2, -0.1]
def summ(e1, e2):
    return e1 * e2
res = sum(list(map(summ, w1, w2)))
print(res)
"""

a = np.array([1, 2, 3, 4])
b = np.array([5, 6, 7, 8])
c = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
d = np.zeros((2, 4))  # Матрица 2*4 заполненная нулями
e = np.random.rand(2, 5)  # Матрица 2*5 заполненная случайными числами от 0 до 1

f = np.zeros((1, 4))
g = np.zeros((4, 3))
cc = f.dot(g)

print(a)
print(b)
print(c)
print(d)
print(e)

print(a * c)
print("cc= ", cc)