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53 lines
1.2 KiB
53 lines
1.2 KiB
# coding: utf-8
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import numpy as np
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def _numerical_gradient_1d(f, x):
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h = 1e-4 # 0.0001
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grad = np.zeros_like(x)
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for idx in range(x.size):
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tmp_val = x[idx]
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x[idx] = float(tmp_val) + h
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fxh1 = f(x) # f(x+h)
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x[idx] = tmp_val - h
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fxh2 = f(x) # f(x-h)
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grad[idx] = (fxh1 - fxh2) / (2*h)
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x[idx] = tmp_val # 还原值
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return grad
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def numerical_gradient_2d(f, X):
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if X.ndim == 1:
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return _numerical_gradient_1d(f, X)
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else:
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grad = np.zeros_like(X)
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for idx, x in enumerate(X):
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grad[idx] = _numerical_gradient_1d(f, x)
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return grad
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def numerical_gradient(f, x):
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h = 1e-4 # 0.0001
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grad = np.zeros_like(x)
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# 多维迭代
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it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
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while not it.finished:
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idx = it.multi_index
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tmp_val = x[idx]
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x[idx] = float(tmp_val) + h
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fxh1 = f(x) # f(x+h)
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x[idx] = tmp_val - h
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fxh2 = f(x) # f(x-h)
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grad[idx] = (fxh1 - fxh2) / (2*h)
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x[idx] = tmp_val # 还原值
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it.iternext()
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return grad |