Python-сообщество

 def train(X, y, syn0, lens0, lens01, lr):
    errorreturn = 0
    for i in range(0, lens0):
        a = 0
        for j in range(0, lens01):
            a = a + syn0[i][j] * X[j]
        b = 1 / (1 + 2.718281828459045235360287471352662497757 ** -a)
        error = y[i] - b
        errorreturn = errorreturn + abs(error)
        delta = (error) * 1 / (1 + 2.718281828459045235360287471352662497757 ** -b) * lr
        for z in range(0, lens01):
            syn0[i][z] = syn0[i][z] + X[z] * delta
    return syn0, errorreturn

 def train(X, y, syn0, lens0, lens01, lr):
    errorreturn = 0
    for i in range(0, lens0):
        a = 0
        for j in range(0, lens01):
            a = a + syn0[i][j] * X[j]
        b = 1 / (1 + 2.718281828459045235360287471352662497757 ** -a)
        error = y[i] - b
        errorreturn = errorreturn + abs(error)
        delta = (error) * 1 / (1 + 2.718281828459045235360287471352662497757 ** -b) * lr
        for z in range(0, lens01):
            syn0[i][z] = syn0[i][z] + X[z] * delta
    return syn0, errorreturn
def predict(syn0, X, lens0, lens01):
    ret = []
    for i in range(0, lens0):
        a = 0
        for j in range(0, lens01):
            a = a + syn0[i][j] * X[j]
        b = 1 / (1 + 2.718281828459045235360287471352662497757 ** -a)
        ret.append(b)
    return ret
import random
def create(x, y):
    syn0 = []
    for z in range(0, x):
        h = []
        for i in range(0, y):
            h.append(random.uniform(-0.1, 0.1))
        syn0.append(h)
    return syn0
syn = create(2, 4)
X = [[0.1, 0.2, 0.3, 0.4],[0.2, 0.3, 0.4, 0.5],[0.4, 0.5, 0.6, 0.7],[0.5,0.6,0.7,0.8]]
y = [[0.5, 0.6],[0.6, 0.7],[0.8, 0.9],[0.9, 1]]
def trainmass2(X, y, syn, lens0, lens01, lr, iter):
    for i in range(iter):
        for j in range(lens01):
            syn, er = train(X[j], y[j], syn, lens0, lens01, lr)
        #print(er)
    return syn, er
trainmass2(X, y, syn, 2, 4, 1, 50000)
h = predict(syn, X[0], 2, 4)
h2 = predict(syn, [0.5, 0.6, 0.7, 0.8], 2, 4)
print(h)
print(h2)

 #include <math.h>
float train(int lens0, int lens01, float* y, float x[lens01], float syn0[lens0][lens01], float lr, float a, int i, int j, int z)
{
     float b;
     float error;
     float errorreturn;
     float delta;
     for ( int i = 0; i < lens0; i++ )
          
          a = 0;
          for ( int j = 0; i < lens01; j++ )
               a = a + syn0[i][j] * x[j];
      
          b = 1/(1+(exp((double) - a)));
          error = y[i] - b;
          errorreturn = errorreturn + error;
          delta = error * 1/(1+(exp((double) - a))) * lr;
          for ( int z = 0; z < lens01; z++ )
               syn0[i][z] = syn0[i][z] + x[z] * delta;
      return syn0[lens0][lens01];      
}

 import ctypes
import random
X = [[0, 0, 1],[0, 1, 1],[1, 0, 1],[1, 1, 1]]
y = [0, 0.1, 1, 0]
syn0= []
for i in range(0,4):
    syn0.append(random.uniform(-0.1,0.1))
syn0 = [syn0]
print(syn0)
fib = ctypes.CDLL('./myfib.so').train
g = fib(1, 4, [y[i]], X[0], [syn0], 0.1, 0, 1, 4, 4)