mirror of
https://github.com/wassname/Run-Skeleton-Run.git
synced 2026-06-27 17:14:10 +08:00
Kolesnikov Sergey
93 lines
2.1 KiB
Python
93 lines
2.1 KiB
Python
import numpy as np
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import scipy.signal
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def discount(x, gamma):
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"""
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computes discounted sums along 0th dimension of x.
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inputs
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------
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x: ndarray
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gamma: float
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outputs
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-------
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y: ndarray with same shape as x, satisfying
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y[t] = x[t] + gamma*x[t+1] + gamma^2*x[t+2] + ... + gamma^k x[t+k],
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where k = len(x) - t - 1
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"""
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assert x.ndim >= 1
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return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
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def explained_variance(ypred, y):
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"""
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Computes fraction of variance that ypred explains about y.
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Returns 1 - Var[y-ypred] / Var[y]
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interpretation:
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ev=0 => might as well have predicted zero
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ev=1 => perfect prediction
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ev<0 => worse than just predicting zero
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"""
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assert y.ndim == 1 and ypred.ndim == 1
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vary = np.var(y)
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return np.nan if vary == 0 else 1 - np.var(y - ypred) / vary
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def explained_variance_2d(ypred, y):
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assert y.ndim == 2 and ypred.ndim == 2
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vary = np.var(y, axis=0)
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out = 1 - np.var(y - ypred) / vary
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out[vary < 1e-10] = 0
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return out
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def ncc(ypred, y):
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return np.corrcoef(ypred, y)[1, 0]
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def flatten_arrays(arrs):
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return np.concatenate([arr.flat for arr in arrs])
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def unflatten_vector(vec, shapes):
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i = 0
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arrs = []
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for shape in shapes:
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size = np.prod(shape)
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arr = vec[i:i + size].reshape(shape)
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arrs.append(arr)
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i += size
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return arrs
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def discount_with_boundaries(X, New, gamma):
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"""
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X: 2d array of floats, time x features
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New: 2d array of bools, indicating when a new episode has started
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"""
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Y = np.zeros_like(X)
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T = X.shape[0]
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Y[T - 1] = X[T - 1]
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for t in range(T - 2, -1, -1):
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Y[t] = X[t] + gamma * Y[t + 1] * (1 - New[t + 1])
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return Y
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def test_discount_with_boundaries():
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gamma = 0.9
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x = np.array([1.0, 2.0, 3.0, 4.0], 'float32')
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starts = [1.0, 0.0, 0.0, 1.0]
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y = discount_with_boundaries(x, starts, gamma)
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assert np.allclose(y, [
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1 + gamma * 2 + gamma ** 2 * 3,
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2 + gamma * 3,
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3,
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4
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])
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