diff --git a/linear_algebra/src/conjugate_gradient.py b/linear_algebra/src/conjugate_gradient.py
index 4c0b58deb978..45da35813978 100644
--- a/linear_algebra/src/conjugate_gradient.py
+++ b/linear_algebra/src/conjugate_gradient.py
@@ -61,7 +61,8 @@ def _create_spd_matrix(dimension: int) -> Any:
>>> _is_matrix_spd(spd_matrix)
True
"""
- random_matrix = np.random.randn(dimension, dimension)
+ rng = np.random.default_rng()
+ random_matrix = rng.normal(size=(dimension, dimension))
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert _is_matrix_spd(spd_matrix)
return spd_matrix
@@ -157,7 +158,8 @@ def test_conjugate_gradient() -> None:
# Create linear system with SPD matrix and known solution x_true.
dimension = 3
spd_matrix = _create_spd_matrix(dimension)
- x_true = np.random.randn(dimension, 1)
+ rng = np.random.default_rng()
+ x_true = rng.normal(size=(dimension, 1))
b = np.dot(spd_matrix, x_true)
# Numpy solution.
diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py
index 7f129919a3ce..e48905eeac6a 100644
--- a/machine_learning/decision_tree.py
+++ b/machine_learning/decision_tree.py
@@ -187,7 +187,8 @@ def main():
tree = DecisionTree(depth=10, min_leaf_size=10)
tree.train(x, y)
- test_cases = (np.random.rand(10) * 2) - 1
+ rng = np.random.default_rng()
+ test_cases = (rng.random(10) * 2) - 1
predictions = np.array([tree.predict(x) for x in test_cases])
avg_error = np.mean((predictions - test_cases) ** 2)
diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py
index 9f6646944458..a926362fc18b 100644
--- a/machine_learning/k_means_clust.py
+++ b/machine_learning/k_means_clust.py
@@ -55,12 +55,12 @@
def get_initial_centroids(data, k, seed=None):
"""Randomly choose k data points as initial centroids"""
- if seed is not None: # useful for obtaining consistent results
- np.random.seed(seed)
+ # useful for obtaining consistent results
+ rng = np.random.default_rng(seed)
n = data.shape[0] # number of data points
# Pick K indices from range [0, N).
- rand_indices = np.random.randint(0, n, k)
+ rand_indices = rng.integers(0, n, k)
# Keep centroids as dense format, as many entries will be nonzero due to averaging.
# As long as at least one document in a cluster contains a word,
diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py
index be16baca1a4c..408d59ab5d29 100644
--- a/machine_learning/sequential_minimum_optimization.py
+++ b/machine_learning/sequential_minimum_optimization.py
@@ -289,12 +289,13 @@ def _choose_a2(self, i1):
if cmd is None:
return
- for i2 in np.roll(self.unbound, np.random.choice(self.length)):
+ rng = np.random.default_rng()
+ for i2 in np.roll(self.unbound, rng.choice(self.length)):
cmd = yield i1, i2
if cmd is None:
return
- for i2 in np.roll(self._all_samples, np.random.choice(self.length)):
+ for i2 in np.roll(self._all_samples, rng.choice(self.length)):
cmd = yield i1, i2
if cmd is None:
return
diff --git a/neural_network/back_propagation_neural_network.py b/neural_network/back_propagation_neural_network.py
index 7e0bdbbe2857..6131a13e945e 100644
--- a/neural_network/back_propagation_neural_network.py
+++ b/neural_network/back_propagation_neural_network.py
@@ -51,8 +51,9 @@ def __init__(
self.is_input_layer = is_input_layer
def initializer(self, back_units):
- self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units)))
- self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T
+ rng = np.random.default_rng()
+ self.weight = np.asmatrix(rng.normal(0, 0.5, (self.units, back_units)))
+ self.bias = np.asmatrix(rng.normal(0, 0.5, self.units)).T
if self.activation is None:
self.activation = sigmoid
@@ -174,7 +175,8 @@ def plot_loss(self):
def example():
- x = np.random.randn(10, 10)
+ rng = np.random.default_rng()
+ x = rng.normal(size=(10, 10))
y = np.asarray(
[
[0.8, 0.4],
diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py
index 07cc456b7466..3c551924442d 100644
--- a/neural_network/convolution_neural_network.py
+++ b/neural_network/convolution_neural_network.py
@@ -41,15 +41,16 @@ def __init__(
self.size_pooling1 = size_p1
self.rate_weight = rate_w
self.rate_thre = rate_t
+ rng = np.random.default_rng()
self.w_conv1 = [
- np.asmatrix(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
+ np.asmatrix(-1 * rng.random((self.conv1[0], self.conv1[0])) + 0.5)
for i in range(self.conv1[1])
]
- self.wkj = np.asmatrix(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
- self.vji = np.asmatrix(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
- self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1
- self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1
- self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1
+ self.wkj = np.asmatrix(-1 * rng.random((self.num_bp3, self.num_bp2)) + 0.5)
+ self.vji = np.asmatrix(-1 * rng.random((self.num_bp2, self.num_bp1)) + 0.5)
+ self.thre_conv1 = -2 * rng.random(self.conv1[1]) + 1
+ self.thre_bp2 = -2 * rng.random(self.num_bp2) + 1
+ self.thre_bp3 = -2 * rng.random(self.num_bp3) + 1
def save_model(self, save_path):
# save model dict with pickle
diff --git a/neural_network/input_data.py b/neural_network/input_data.py
index 9d4195487dbb..d189e3f9e0d9 100644
--- a/neural_network/input_data.py
+++ b/neural_network/input_data.py
@@ -153,7 +153,7 @@ def __init__(
"""
seed1, seed2 = random_seed.get_seed(seed)
# If op level seed is not set, use whatever graph level seed is returned
- np.random.seed(seed1 if seed is None else seed2)
+ self._rng = np.random.default_rng(seed1 if seed is None else seed2)
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
@@ -211,7 +211,7 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
perm0 = np.arange(self._num_examples)
- np.random.shuffle(perm0)
+ self._rng.shuffle(perm0)
self._images = self.images[perm0]
self._labels = self.labels[perm0]
# Go to the next epoch
@@ -225,7 +225,7 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
# Shuffle the data
if shuffle:
perm = np.arange(self._num_examples)
- np.random.shuffle(perm)
+ self._rng.shuffle(perm)
self._images = self.images[perm]
self._labels = self.labels[perm]
# Start next epoch
diff --git a/neural_network/two_hidden_layers_neural_network.py b/neural_network/two_hidden_layers_neural_network.py
index dea7e2342d9f..d488de590cc2 100644
--- a/neural_network/two_hidden_layers_neural_network.py
+++ b/neural_network/two_hidden_layers_neural_network.py
@@ -28,19 +28,20 @@ def __init__(self, input_array: np.ndarray, output_array: np.ndarray) -> None:
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
- self.input_layer_and_first_hidden_layer_weights = np.random.rand(
- self.input_array.shape[1], 4
+ rng = np.random.default_rng()
+ self.input_layer_and_first_hidden_layer_weights = rng.random(
+ (self.input_array.shape[1], 4)
)
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
- self.first_hidden_layer_and_second_hidden_layer_weights = np.random.rand(4, 3)
+ self.first_hidden_layer_and_second_hidden_layer_weights = rng.random((4, 3))
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
- self.second_hidden_layer_and_output_layer_weights = np.random.rand(3, 1)
+ self.second_hidden_layer_and_output_layer_weights = rng.random((3, 1))
# Real output values provided.
self.output_array = output_array
diff --git a/pyproject.toml b/pyproject.toml
index 22da7cb777b5..827cea1fcf37 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,6 @@ lint.ignore = [ # `ruff rule S101` for a description of that rule
"EXE001", # Shebang is present but file is not executable" -- FIX ME
"G004", # Logging statement uses f-string
"INP001", # File `x/y/z.py` is part of an implicit namespace package. Add an `__init__.py`. -- FIX ME
- "NPY002", # Replace legacy `np.random.choice` call with `np.random.Generator` -- FIX ME
"PGH003", # Use specific rule codes when ignoring type issues -- FIX ME
"PLC1901", # `{}` can be simplified to `{}` as an empty string is falsey
"PLW060", # Using global for `{name}` but no assignment is done -- DO NOT FIX