compressed_K = new_system.compressed_K
compressed_s = new_system.compressed_s
print(f"truncation_index = {new_system.truncation_index}")
# %%
# Fista LASSO cross-validation
# '''''''''''''''''''''''''''''
# Create a guess range of values for the :math:`\lambda` hyperparameters.
lambdas = 10**(-5 + 4 * (np.arange(64) / 63))
# setup the smooth lasso cross-validation class
f_lasso_cv = LassoFistaCV(
lambdas=lambdas, # A numpy array of lambda values.
folds=5, # The number of folds in n-folds cross-validation.
sigma=sigma, # noise standard deviation
inverse_dimension=
inverse_dimension, # previously defined inverse dimensions.
)
# run the fit method on the compressed kernel and compressed data.
f_lasso_cv.fit(K=compressed_K, s=compressed_s)
# %%
# The optimum hyper-parameters
# ''''''''''''''''''''''''''''
print(f_lasso_cv.hyperparameters)
# %%
# The cross-validation curve
# ''''''''''''''''''''''''''
print(f"truncation_index = {new_system.truncation_index}")
# %%
# Solving the inverse problem
# ---------------------------
# FISTA LASSO cross-validation
# '''''''''''''''''''''''''''''
# setup the pre-defined range of alpha and lambda values
lambdas = 10**(-4 + 5 * (np.arange(32) / 31))
# setup the smooth lasso cross-validation class
s_lasso = LassoFistaCV(
lambdas=lambdas, # A numpy array of lambda values.
sigma=sigma, # data standard deviation
folds=5, # The number of folds in n-folds cross-validation.
inverse_dimension=
inverse_dimension, # previously defined inverse dimensions.
)
# run the fit method on the compressed kernel and compressed data.
s_lasso.fit(K=compressed_K, s=compressed_s)
# %%
# The optimum hyper-parameters
# ''''''''''''''''''''''''''''
print(s_lasso.hyperparameters)
# %%
# The cross-validation curve
# ''''''''''''''''''''''''''