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Changed cp_als() param 'tensor' to 'input_tensor' to avoid ambiguity #118

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merged 2 commits into from
May 31, 2023
Merged

Changed cp_als() param 'tensor' to 'input_tensor' to avoid ambiguity #118

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ca9022f
Changed cp_als() param 'tensor' to 'input_tensor' to avoid ambiguity
DeepBlockDeepak May 31, 2023
6891454
Formatted changes with isort and black.
DeepBlockDeepak May 31, 2023
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20 changes: 10 additions & 10 deletions pyttb/cp_als.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@


def cp_als(
tensor,
input_tensor,
rank,
stoptol=1e-4,
maxiters=1000,
Expand Down Expand Up @@ -114,8 +114,8 @@ def cp_als(
"""

# Extract number of dimensions and norm of tensor
N = tensor.ndims
normX = tensor.norm()
N = input_tensor.ndims
normX = input_tensor.norm()

# Set up dimorder if not specified
if not dimorder:
Expand All @@ -139,17 +139,17 @@ def cp_als(
init.ncomponents == rank
), "Initial guess does not have {} components".format(rank)
for n in dimorder:
if init.factor_matrices[n].shape != (tensor.shape[n], rank):
if init.factor_matrices[n].shape != (input_tensor.shape[n], rank):
assert False, "Mode {} of the initial guess is the wrong size".format(n)
elif isinstance(init, str) and init.lower() == "random":
factor_matrices = []
for n in range(N):
factor_matrices.append(np.random.uniform(0, 1, (tensor.shape[n], rank)))
factor_matrices.append(np.random.uniform(0, 1, (input_tensor.shape[n], rank)))
init = ttb.ktensor.from_factor_matrices(factor_matrices)
elif isinstance(init, str) and init.lower() == "nvecs":
factor_matrices = []
for n in range(N):
factor_matrices.append(tensor.nvecs(n, rank))
factor_matrices.append(input_tensor.nvecs(n, rank))
init = ttb.ktensor.from_factor_matrices(factor_matrices)
else:
assert False, "The selected initialization method is not supported"
Expand All @@ -159,7 +159,7 @@ def cp_als(
fit = 0

# Store the last MTTKRP result to accelerate fitness computation
U_mttkrp = np.zeros((tensor.shape[dimorder[-1]], rank))
U_mttkrp = np.zeros((input_tensor.shape[dimorder[-1]], rank))

if printitn > 0:
print("CP_ALS:")
Expand All @@ -176,7 +176,7 @@ def cp_als(
# Iterate over all N modes of the tensor
for n in dimorder:
# Calculate Unew = X_(n) * khatrirao(all U except n, 'r').
Unew = tensor.mttkrp(U, n)
Unew = input_tensor.mttkrp(U, n)

# Save the last MTTKRP result for fitness check.
if n == dimorder[-1]:
Expand Down Expand Up @@ -245,11 +245,11 @@ def cp_als(

if printitn > 0:
if normX == 0:
normresidual = M.norm() ** 2 - 2 * tensor.innerprod(M)
normresidual = M.norm() ** 2 - 2 * input_tensor.innerprod(M)
fit = normresidual
else:
normresidual = np.sqrt(
np.abs(normX**2 + M.norm() ** 2 - 2 * tensor.innerprod(M))
np.abs(normX**2 + M.norm() ** 2 - 2 * input_tensor.innerprod(M))
)
fit = 1 - (normresidual / normX) # fraction explained by model
print(f" Final f = {fit:e}")
Expand Down