import tensorflow as tf
[docs]
def zero_diag(mat: tf.Tensor) -> tf.Tensor:
"""
Set the diagonal of a matrix to 0
Parameters
----------
mat
A 2D square matrix
Returns
-------
A 2D square matrix with zeros along the diagonal
"""
return mat - tf.linalg.diag(tf.linalg.diag_part(mat))
[docs]
def quantile(sample: tf.Tensor, p: float, type: int = 7, sorted: bool = False) -> float:
"""
Estimate a desired quantile of a univariate distribution from a vector of samples
Parameters
----------
sample
A 1D vector of values
p
The desired quantile in (0,1)
type
The method for computing the quantile.
See https://wikipedia.org/wiki/Quantile#Estimating_quantiles_from_a_sample
sorted
Whether or not the vector is already sorted into ascending order
Returns
-------
An estimate of the quantile
"""
N = len(sample)
if len(sample.shape) != 1:
raise ValueError("Quantile estimation only supports vectors of univariate samples.")
if not 1/N <= p <= (N-1)/N:
raise ValueError(f"The {p}-quantile should not be estimated using only {N} samples.")
sorted_sample = sample if sorted else tf.sort(sample)
if type == 6:
h = (N+1)*p
elif type == 7:
h = (N-1)*p + 1
elif type == 8:
h = (N+1/3)*p + 1/3
h_floor = int(h)
quantile = sorted_sample[h_floor-1]
if h_floor != h:
quantile += (h - h_floor)*(sorted_sample[h_floor]-sorted_sample[h_floor-1])
return float(quantile)
[docs]
def subset_matrix(mat: tf.Tensor, inds_0: tf.Tensor, inds_1: tf.Tensor) -> tf.Tensor:
"""
Take a matrix and return the submatrix correspond to provided row and column indices
Parameters
----------
mat
A 2D matrix
inds_0
A vector of row indices
inds_1
A vector of column indices
Returns
-------
A submatrix of shape (len(inds_0), len(inds_1))
"""
if len(mat.shape) != 2:
raise ValueError("Subsetting only supported for matrices (2D)")
subbed_rows = tf.gather(mat, inds_0, axis=0)
subbed_rows_cols = tf.gather(subbed_rows, inds_1, axis=1)
return subbed_rows_cols
[docs]
def clone_model(model: tf.keras.Model) -> tf.keras.Model:
""" Clone a sequential, functional or subclassed tf.keras.Model. """
try: # sequential or functional model
return tf.keras.models.clone_model(model)
except ValueError: # subclassed model
try:
config = model.get_config()
except NotImplementedError:
config = {}
return model.__class__.from_config(config)