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# import random
# x_data = np.array([12,15,19,24,27]).reshape(5,1)
# t_data = np.array([150,178,210,245,300]).reshape(5,1)
# W = np.random.rand(1)
# b = np.random.rand(1)
# print("W= ",W, "W.shape,",W.shape,", b=",b,"b.shape= ", b.shape)
# def loss_func(x,t):
# y= np.dot(x,W)+b
# return(np.sum((t-y)**2))/(len(x))
# def numerical_derivative(f,x):
# delta_x = 1e-4
# grad = np.zeros_like(x)
# it = np.nditer(x,flags= ['multi_index'], op_flags=['readwrite'])
# while not it.finished:
# idx = it.multi_index
# tmp_val =x[idx]
# x[idx] = float(tmp_val) + delta_x
# fx1 = f(x)
# x[idx] = tmp_val - delta_x
# fx2 = f(x)
# grad[idx] = (fx1 - fx2)/ (2*delta_x)
# x[idx] = tmp_val
# it.iternext()
# return grad
# def error_val(x,t):
# y = np.dot(x, W) + b
# return (np.sum ((t-y)**2))/(len(x))
# def predict(x):
# y = np.dot(x, W) + b
# return y
# learning_rate = 1e-2
# f = lambda x : loss_func(x_data, t_data)
# print("Initial error value = ", error_val(x_data, t_data), "initial W = ",W, "b = ", b)
# for step in range(8001):
# W -= learning_rate * numerical_derivative (f,W)
# b -= learning_rate * numerical_derivative (f,b)
# if (step % 400 == 0):
# print("step = ", step, "error value = ", error_val(x_data,t_data), "W=", W, "b=", b)
# print(predict(23))
# print(predict(50))
# from unicodedata import numeric
# from cgitb import reset
# import numpy as np
# import random
# x_data = np.array([[93,53,69,78,86,96],[88,46,70,75,82,93]]).reshape(6,2)
# t_data = np.array([185,101,141,148,175,192]).reshape(6,1)
# W = np.random.rand(2,1)
# b = np.random.rand(1)
# print("W= ",W, "W=.shape,",W.shape,",b=",b,"b.shape= ", b.shape)
# def loss_func(x,t):
# y= np.dot(x,W)+b
# return(np.sum((t-y)**2))/(len(x))
# def numerical_derivative(f,x):
# delta_x = 1e-4
# grad = np.zeros_like(x)
# it = np.nditer(x,flags= ['multi_index'], op_flags=['readwrite'])
# while not it.finished:
# idx =it.multi_index
# tmp_val =x[idx]
# x[idx] = float(tmp_val) + delta_x
# fx1 = f(x)
# x[idx] = tmp_val - delta_x
# fx2 = f(x)
# grad[idx] - (fx1 - fx2)/ (2*delta_x)
# x[idx] = tmp_val
# it.iternext()
# return grad
# def error_val(x,t):
# y = np.dot(x, W) + b
# return (np.sum ((t-y)**2))/(len(x))
# def predict(x):
# y = np.dot(x, W) + b
# return y
# learning_rate = 1e-2
# f = lambda x : loss_func(x_data, t_data)
# print("Initial error value = ", error_val(x_data, t_data), "initial W = ",W, "b = ", b)
# for step in range(8001):
# W -= learning_rate * numerical_derivative (f,W)
# b -= learning_rate * numerical_derivative (f,b)
# if (step % 400 == 0):
# print("step = ", step, "error value = ", error_val(x_data,t_data), "W=", W, "b=", b)
# test_data = np.array([87,79])
# result = predict(test_data)
# print("predict data is", result)
# test_data = np.array([73,80])
# result = predict(test_data)
# print("predict data is", result)
# test_data = np.array([81,90])
# result = predict(test_data)
# print("predict data is", result)
from unittest import result
import numpy as np
import random
loaded_data = np.loadtxt("data_0.csv", delimiter=",")
x_data = loaded_data[:, 0:-1]
t_data = loaded_data[:, [-1]]
W = np.random.rand(3,1)
b = np.random.rand(1)
def loss_func(x,t):
y= np.dot(x,W)+b
return(np.sum((t-y)**2))/(len(x))
def numerical_derivative(f,x):
delta_x = 1e-4
grad = np.zeros_like(x)
it = np.nditer(x,flags= ['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx =it.multi_index
tmp_val =x[idx]
x[idx] = float(tmp_val) + delta_x
fx1 = f(x)
x[idx] = tmp_val - delta_x
fx2 = f(x)
grad[idx] - (fx1 - fx2)/ (2*delta_x)
x[idx] = tmp_val
it.iternext()
return grad
def error_val(x,t):
y = np.dot(x, W) + b
return (np.sum ((t-y)**2))/(len(x))
def predict(x):
y = np.dot(x, W) + b
return y
learning_rate = 1e-5
f = lambda x : loss_func(x_data, t_data)
print("Initial error value = ", error_val(x_data, t_data), "initial W = ",W, "b = ", b)
for step in range(8001):
W -= learning_rate * numerical_derivative (f,W)
b -= learning_rate * numerical_derivative (f,b)
if (step % 400 == 0):
print("step = ", step, "error value = ", error_val(x_data,t_data), "W=", W, "b=", b)
test_data = np.array([100,98,81])
result = predict(test_data)
print("predict data is", result)
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