pypots/forecasting/bttf/submodules.py
Function sample_factor_x
has a Cognitive Complexity of 12 (exceeds 5 allowed). Consider refactoring. Open
Open
def sample_factor_x(tau_sparse_tensor, tau_ind, time_lags, U, V, X, A, Lambda_x):
"""Sampling T-by-R factor matrix X."""
dim3, rank = X.shape
tmax = np.max(time_lags)
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Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"
Further reading
Function sample_factor_x
has 8 arguments (exceeds 4 allowed). Consider refactoring. Open
Open
def sample_factor_x(tau_sparse_tensor, tau_ind, time_lags, U, V, X, A, Lambda_x):
Function sample_factor_v
has 6 arguments (exceeds 4 allowed). Consider refactoring. Open
Open
def sample_factor_v(tau_sparse_tensor, tau_ind, U, V, X, beta0=1):
Function sample_factor_u
has 6 arguments (exceeds 4 allowed). Consider refactoring. Open
Open
def sample_factor_u(tau_sparse_tensor, tau_ind, U, V, X, beta0=1):
Function ar4cast
has 5 arguments (exceeds 4 allowed). Consider refactoring. Open
Open
def ar4cast(A, X, Sigma, time_lags, multi_step):