tfp.substrates.numpy.glm.compute_predicted_linear_response
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Computes model_matrix @ model_coefficients + offset
.
tfp.substrates.numpy.glm.compute_predicted_linear_response(
model_matrix, model_coefficients, offset=None, name=None
)
Args |
model_matrix
|
(Batch of) float -like, matrix-shaped Tensor where each row
represents a sample's features.
|
model_coefficients
|
(Batch of) vector-shaped Tensor representing the model
coefficients, one for each column in model_matrix . Must have same
dtype as model_matrix .
|
offset
|
Optional Tensor representing constant shift applied to
predicted_linear_response . Must broadcast to response .
Default value: None (i.e., tf.zeros_like(predicted_linear_response) ).
|
name
|
Python str used as name prefix to ops created by this function.
Default value: None (i.e., "compute_predicted_linear_response" ).
|
Returns |
predicted_linear_response
|
response -shaped Tensor representing linear
predictions based on new model_coefficients , i.e.,
tf.linalg.matvec(model_matrix, model_coefficients) + offset .
|
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Last updated 2023-11-21 UTC.
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