tfp.glm.fit_one_step

Runs one step of Fisher scoring.

tfp.glm.fit_one_step(
    model_matrix, response, model, model_coefficients_start=None,
    predicted_linear_response_start=None, l2_regularizer=None, dispersion=None,
    offset=None, learning_rate=None, fast_unsafe_numerics=True,
    l2_regularization_penalty_factor=None, name=None
)

Args
`model_matrix`	(Batch of) `float`-like, matrix-shaped `Tensor` where each row represents a sample's features.
`response`	(Batch of) vector-shaped `Tensor` where each element represents a sample's observed response (to the corresponding row of features). Must have same `dtype` as `model_matrix`.
`model`	`tfp.glm.ExponentialFamily`-like instance used to construct the negative log-likelihood loss, gradient, and expected Hessian (i.e., the Fisher information matrix).
`model_coefficients_start`	Optional (batch of) vector-shaped `Tensor` representing the initial model coefficients, one for each column in `model_matrix`. Must have same `dtype` as `model_matrix`. Default value: Zeros.
`predicted_linear_response_start`	Optional `Tensor` with `shape`, `dtype` matching `response`; represents `offset` shifted initial linear predictions based on `model_coefficients_start`. Default value: `offset` if `model_coefficients is None`, and `tf.linalg.matvec(model_matrix, model_coefficients_start) + offset` otherwise.
`l2_regularizer`	Optional scalar `Tensor` representing L2 regularization penalty, i.e., `loss(w) = sum{-log p(y[i]\|x[i],w) : i=1..n} + l2_regularizer \|\|w\|\|_2^2`. Default value: `None` (i.e., no L2 regularization).
`dispersion`	Optional (batch of) `Tensor` representing `response` dispersion, i.e., as in, `p(y\|theta) := exp((y theta - A(theta)) / dispersion)`. Must broadcast with rows of `model_matrix`. Default value: `None` (i.e., "no dispersion").
`offset`	Optional `Tensor` representing constant shift applied to `predicted_linear_response`. Must broadcast to `response`. Default value: `None` (i.e., `tf.zeros_like(response)`).
`learning_rate`	Optional (batch of) scalar `Tensor` used to dampen iterative progress. Typically only needed if optimization diverges, should be no larger than `1` and typically very close to `1`. Default value: `None` (i.e., `1`).
`fast_unsafe_numerics`	Optional Python `bool` indicating if solve should be based on Cholesky or QR decomposition. Default value: `True` (i.e., "prefer speed via Cholesky decomposition").
`l2_regularization_penalty_factor`	Optional (batch of) vector-shaped `Tensor`, representing a separate penalty factor to apply to each model coefficient, length equal to columns in `model_matrix`. Each penalty factor multiplies l2_regularizer to allow differential regularization. Can be 0 for some coefficients, which implies no regularization. Default is 1 for all coefficients. `loss(w) = sum{-log p(y[i]\|x[i],w) : i=1..n} + l2_regularizer \|\|w * l2_regularization_penalty_factor\|\|_2^2`
`name`	Python `str` used as name prefix to ops created by this function. Default value: `"fit_one_step"`.

Returns
`model_coefficients`	(Batch of) vector-shaped `Tensor`; represents the next estimate of the model coefficients, one for each column in `model_matrix`.
`predicted_linear_response`	`response`-shaped `Tensor` representing linear predictions based on new `model_coefficients`, i.e., `tf.linalg.matvec(model_matrix, model_coefficients_next) + offset`.

tfp.glm.fit_one_step

Args

Returns