AltLuv commited on
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End of training

Browse files
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+ import jax.numpy as jnp
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+ import jax
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+ import torch
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+ from dataclasses import dataclass
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+ import sympy
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+ import sympy as sp
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+ from sympy import Matrix, Symbol
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+ import math
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+ from sde_redefined_param import SDEDimension
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+ @dataclass
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+ class SDEParameterizedMaxNoiseBaseLineConfig:
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+ name = "Custom"
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+ variable = Symbol('t', nonnegative=True, real=True)
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+
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+ drift_dimension = SDEDimension.SCALAR
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+ diffusion_dimension = SDEDimension.SCALAR
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+ diffusion_matrix_dimension = SDEDimension.SCALAR
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+
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+ # TODO (KLAUS): HANDLE THE PARAMETERS BEING Ø
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+ drift_parameters = Matrix([sympy.symbols("f1", real=True)])
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+ diffusion_parameters = Matrix([sympy.symbols("sigma_max", real=True, nonzero=True)])
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+
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+ drift = 0
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+
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+ sigma_min = 0.002
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+ sigma_max = sympy.Abs(diffusion_parameters[0]) #80
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+ diffusion = sigma_min * (sigma_max/sigma_min)**variable * sympy.sqrt(2 * sympy.Abs(sympy.log(sigma_max/sigma_min)))
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+
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+ # TODO (KLAUS) : in the SDE SAMPLING CHANGING Q impacts how we sample z ~ N(0, Q*(delta t))
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+ diffusion_matrix = 1
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+
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+ initial_variable_value = 0
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+ max_variable_value = 1 # math.inf
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+ min_sample_value = 0
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+
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+ module = 'jax'
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+
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+ drift_integral_form=False
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+ diffusion_integral_form=False
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+ diffusion_integral_decomposition = 'cholesky' # ldl
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+
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+ non_symbolic_parameters = {'diffusion': torch.tensor([80.])}
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+
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+ target = "epsilon" # x0
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