Making science, Making science
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10
test.py
10
test.py
@ -133,7 +133,7 @@ class Perlin_Noise():
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class Perlin_PCA_Noise():
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def __init__(self, dim_a=2, kernel_func='SE_1.41_1.0', window=128, ssf=-1, f_sigma=1):
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def __init__(self, dim_a=2, kernel_func='SE_1.414_1.0', window=32, ssf=-1, f_sigma=1):
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self.dim_a = dim_a
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self.kernel_func = kernel_func
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self.window = window
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@ -162,7 +162,7 @@ class Perlin_PCA_Noise():
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class PCA_Noise():
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def __init__(self, dim_a=2, kernel_func='SE_1.41_1.0', window=128, ssf=-1, f_sigma=1):
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def __init__(self, dim_a=2, kernel_func='SE_1.414_1.0', window=32, ssf=-1, f_sigma=1):
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self.dim_a = dim_a
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self.kernel_func = kernel_func
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self.window = window
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@ -188,7 +188,7 @@ class PCA_Noise():
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class Human_PCA_Noise():
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def __init__(self, dim_a=2, kernel_func='SE_1.414_1.0', window=128, ssf=-1, f_sigma=1):
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def __init__(self, dim_a=2, kernel_func='SE_1.414_1.0', window=32, ssf=-1, f_sigma=1):
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self.dim_a = dim_a
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self.kernel_func = kernel_func
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self.window = window
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@ -203,7 +203,7 @@ class Human_PCA_Noise():
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traj = th.Tensor(self.traj).unsqueeze(0)
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eps = human_input(obs, env)
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epsilon = th.Tensor(eps).unsqueeze(0)
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sample = self.dist.sample(traj, self.f_sigma, epsilon).squeeze(0)
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sample = self.dist.sample(traj, self.f_sigma, epsilon=epsilon).squeeze(0)
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self.traj.append(sample)
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self.index += 1
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return sample
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@ -217,7 +217,7 @@ class Human_PCA_Noise():
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class Colored_PCA_Noise():
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def __init__(self, beta=1, dim_a=2, samples=2**18, kernel_func='SE_1.41_1', window=64, ssf=-1):
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def __init__(self, beta=1, dim_a=2, samples=2**18, kernel_func='SE_1.41_1.0', window=32, ssf=-1):
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self.beta = beta
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self.dim_a = dim_a
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self.samples = samples
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