Fit the model to X with measurement structure and latent factors’ scales.
Parameters:
X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples
and n_features is the number of measurement features.
G_sign (array-like, shape (n_features, n_features_latent)) – Measurement structure matrix, where n_features_latent is
the number of latent factors and n_features is the
number of measurement features.
scale (array-like, shape (1, n_features_latent)) – Scales of the latent factors.
Estimated measurement matrix between measurement variables and
latent factors.
Returns:
measurement_matrix_ – The measurement matrix between measurement variables and
latent factors, where n_features_latent is the
number of latent factors and n_features is the
number of measurement variables.
Fit the model to X with measurement structure and latent factors’ scales.
Parameters:
X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples
of all domains and n_features is the number of features
of all domains.
G_sign (array-like, shape (n_features, n_features_latent)) – Measurement structure matrix, where n_features_latent is
the number of latent factors of all domains and n_features
is the number of measurement variables of all domains.
scale (array-like, shape (1, n_features_latent)) – Scales of the latent factors.
Estimated measurement matrix between measurement variables and
latent factors from all domains.
Returns:
measurement_matrix_ – The measurement matrix between measurement variables and
latent factors, where n_features_latent is the
number of latent factors and n_features is the
number of measurement variables from all domains.