python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
import torch
import numpy as np
from torch.nn import Parameter
from halp.layers.pool_layer import BitCenterMaxPool2D, BitCenterAvgPool2D
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from unittest import TestCase
from halp.utils.utils import set_seed
from halp.utils.test_utils imp... | halp-master | halp/layers/pool_layer_test.py |
import torch
import numpy as np
import torch.nn.functional as F
from torch.nn import Parameter
from halp.layers.ele_mult import BitCenterEleMult, bit_center_ele_mult
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from unittest import TestCase
from halp.layers.bit_center_layer_test ... | halp-master | halp/layers/ele_mult_test.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
import numpy as np
from halp.layers.linear_layer import BitCenterLinear, bit_center_linear
from torch.autograd import gradcheck
from halp.utils.utils import void_cast_func, single_to_hal... | halp-master | halp/layers/bit_center_layer_test.py |
import torch
import torch.nn as nn
from math import floor
from torch.nn import Conv2d
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.autograd import Function
from torch.autograd import Variable
import numpy as np
from halp.utils.utils import void_cast_func, single_to_half_det, singl... | halp-master | halp/layers/conv_layer.py |
import torch
import torch.nn as nn
from torch.nn import Tanh
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.autograd import Function
import numpy as np
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from halp.layers.bit_center_layer import BitCe... | halp-master | halp/layers/sigmoid_layer.py |
import torch
import torch.nn as nn
from torch.nn import Tanh
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.autograd import Function
import numpy as np
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from halp.layers.bit_center_layer import BitCe... | halp-master | halp/layers/tanh_layer.py |
import torch
import numpy as np
from torch.nn import Parameter
from halp.layers.relu_layer import BitCenterReLU, bit_center_relu
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from unittest import TestCase
from halp.utils.utils import set_seed
from halp.utils.test_utils import Halp... | halp-master | halp/layers/relu_layer_test.py |
import torch
import numpy as np
import copy, logging
from torch.autograd import Variable
from torch.optim.optimizer import required, Optimizer
from torch.optim import SGD
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from halp.optim.bit_center_sgd import BitCenterOptim
import logg... | halp-master | halp/optim/bit_center_svrg.py |
from torch.optim.optimizer import Optimizer, required
import torch
from torch.autograd import Variable
import copy, logging
class SVRG(torch.optim.SGD):
"""Implements stochastic variance reduction gradient descent.
Args:
params (iterable): iterable of parameters to optimize
lr (float): learnin... | halp-master | halp/optim/svrg.py |
halp-master | halp/optim/__init__.py | |
import torch
import torch.nn as nn
import numpy as np
from halp.utils.test_utils import HalpTest
from halp.optim.bit_center_sgd import BitCenterSGD
from halp.optim.bit_center_svrg import BitCenterSVRG
from unittest import TestCase
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from... | halp-master | halp/optim/bit_center_optim_test.py |
import torch
import numpy as np
import copy, logging
from torch.autograd import Variable
from torch.optim.optimizer import required, Optimizer
from torch.optim import SGD
from halp.utils.utils import void_cast_func, single_to_half_det, single_to_half_stoc
from halp.utils.utils import get_recur_attr
import logging
impor... | halp-master | halp/optim/bit_center_sgd.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
from torch.autograd import gradcheck
from halp.layers.bit_center_layer import BitCenterModule, BitCenterModuleList
from halp.layers.linear_layer import BitCenterLinear
from halp.utils.utils import void_cast_func, single_to_half_de... | halp-master | halp/utils/test_utils.py |
import torch
import numpy as np
import logging
import sys
import math
from halp.optim.bit_center_sgd import BitCenterOptim, BitCenterSGD
from halp.optim.bit_center_svrg import BitCenterSVRG
from halp.optim.svrg import SVRG
from halp.utils.utils import get_recur_attr
logging.basicConfig(stream=sys.stdout, level=logging.... | halp-master | halp/utils/train_utils.py |
halp-master | halp/utils/__init__.py | |
import nltk
from nltk.stem import PorterStemmer
import numpy as np
import sys, os
import torch
from halp.utils.utils import set_seed
from halp.utils.utils import DOUBLE_PREC_DEBUG_EPOCH_LEN, LP_DEBUG_EPOCH_LEN
from torch.utils.data.dataset import Dataset
import _pickle as cp
import logging
logging.basicConfig(stream=sy... | halp-master | halp/utils/postag_data_utils.py |
import gzip
import os
from os import path
import numpy as np
import torch
import sys
if sys.version_info.major < 3:
import urllib
else:
import urllib.request as request
from halp.utils.utils import LP_DEBUG_EPOCH_LEN, DOUBLE_PREC_DEBUG_EPOCH_LEN
DATASET_DIR = 'datasets/'
MNIST_FILES = ["train-images-idx3-u... | halp-master | halp/utils/mnist_data_utils.py |
import torch
import numpy as np
import ctypes
from unittest import TestCase
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging.getLogger('')
# DOUBLE_PREC_DEBUG = False
DOUBLE_PREC_DEBUG_EPOCH_LEN = 3
# LP_DEBUG = False
LP_DEBUG_EPOCH_LEN = 3
def single_to_half_de... | halp-master | halp/utils/utils.py |
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
from halp.utils.utils import LP_DEBUG_EPOCH_LEN, DOUBLE_PREC_DEBUG_EPOCH_LEN
import sys
import logging
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging.getLogger('')
def get_cifar10_data_loader(b... | halp-master | halp/utils/cifar_data_utils.py |
import torch
import numpy as np
from torch.autograd import Variable
# from halp.utils.test_utils import assert_model_grad_equal
from halp.utils.utils import get_recur_attr
from halp.utils.utils import single_to_half_det, single_to_half_stoc, void_cast_func
from halp.utils.utils import copy_model_weights, set_seed
from ... | halp-master | halp/models/model_test.py |
halp-master | halp/models/__init__.py | |
import torch
import numpy as np
from torch.autograd import Variable
from halp.utils.utils import single_to_half_det, single_to_half_stoc, void_cast_func
from halp.utils.utils import copy_model_weights, set_seed
from unittest import TestCase
from halp.utils.test_utils import HalpTest
from halp.models.lenet import LeNet_... | halp-master | halp/models/lenet_test.py |
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from halp.utils.utils import single_to_half_det, single_to_half_stoc, copy_layer_weights
from halp.utils.utils import void_cast_func, get_recur_attr
from halp.layers.bit_center_layer import BitCent... | halp-master | halp/models/lenet.py |
import torch
import numpy as np
from torch.autograd import Variable
import halp.utils.utils
from halp.utils.utils import single_to_half_det, single_to_half_stoc
from halp.models.logistic_regression import LogisticRegression
from unittest import TestCase
class LeNetTest(TestCase):
def test_logistic_regression_gra... | halp-master | halp/models/logistic_regression_test.py |
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from halp.utils.utils import single_to_half_det, single_to_half_stoc
from halp.utils.utils import copy_layer_weights, copy_module_weights
from halp.utils.utils import void_cast_func, get_recur_attr
... | halp-master | halp/models/resnet.py |
import torch
import numpy as np
from torch.autograd import Variable
from halp.utils.utils import single_to_half_det, single_to_half_stoc, void_cast_func
from halp.layers.bit_center_layer import BitCenterModule
from halp.layers.linear_layer import BitCenterLinear
from halp.layers.cross_entropy import BitCenterCrossEntro... | halp-master | halp/models/logistic_regression.py |
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.autograd import Variable
from halp.utils.utils import single_to_half_det, single_to_half_stoc
from halp.utils.utils import copy_layer_weights, copy_module_weights
from halp.utils.uti... | halp-master | halp/models/lstm.py |
import torch
import numpy as np
from torch.autograd import Variable
# from halp.utils.test_utils import assert_model_grad_equal
from halp.utils.utils import single_to_half_det, single_to_half_stoc, void_cast_func
from halp.utils.utils import copy_model_weights, set_seed, get_recur_attr
from unittest import TestCase
fro... | halp-master | halp/models/lstm_test.py |
import torch
import numpy as np
from torch.autograd import Variable
# from halp.utils.test_utils import assert_model_grad_equal
from halp.utils.utils import single_to_half_det, single_to_half_stoc, void_cast_func
from halp.utils.utils import copy_model_weights, set_seed, get_recur_attr
from unittest import TestCase
fro... | halp-master | halp/models/resnet_test.py |
import copy
import argparse
import math
import numpy as np
import torch
torch.backends.cudnn.deterministic=True
import torch.nn as nn
import torch.utils.data
from torch.optim import SGD
import halp
from halp.optim.bit_center_sgd import BitCenterSGD
from halp.optim.bit_center_svrg import BitCenterSVRG
from halp.optim.sv... | halp-master | halp/exp_script/run_models.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import platform
import re
import sys
from glob import glob
from pybind11.setup_helpers import build_ext, Pybind11Extension
from s... | beanmachine-main | setup.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# ... | beanmachine-main | website/sphinx/conf.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import re
import shutil
import uuid
import warnings
from pathlib import Path
from typing import Any, Dict, List, Tuple, Union
i... | beanmachine-main | website/scripts/convert_ipynb_to_mdx.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch.distributions as dist
from torch import tensor
class ToplevelSmokeTest(unittest... | beanmachine-main | tests/ppl/smoke_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import pytest
import torch.distributions as dist
@pytest.fixture(autouse=True)
def fix_random_seed():
"""... | beanmachine-main | tests/ppl/conftest.py |
beanmachine-main | tests/ppl/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import pytest
import torch
from beanmachine.ppl.experimental.torch_jit_backend import get_backend, TorchJITBac... | beanmachine-main | tests/ppl/experimental/torch_jit_backend_test.py |
beanmachine-main | tests/ppl/experimental/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.split_rule import (
CompositeRules,
Dimen... | beanmachine-main | tests/ppl/experimental/bart/bart_split_rule_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.scalar_samplers import (
NoiseStandardDeviatio... | beanmachine-main | tests/ppl/experimental/bart/bart_scalar_sampler_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.exceptions import (
GrowError,
PruneErro... | beanmachine-main | tests/ppl/experimental/bart/bart_tree_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from copy import deepcopy
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.exceptions import (
... | beanmachine-main | tests/ppl/experimental/bart/bart_node_test.py |
beanmachine-main | tests/ppl/experimental/bart/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.exceptions import (
PruneError,
)
from beanm... | beanmachine-main | tests/ppl/experimental/bart/bart_tree_proposer_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.bart_model import (
LeafMean,
)
from beanmach... | beanmachine-main | tests/ppl/experimental/bart/xbart_grow_from_root_proposer_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from beanmachine.ppl.experimental.causal_inference.models.bart.bart_model import (
BART,
XBART,
)
@pyt... | beanmachine-main | tests/ppl/experimental/bart/bart_model_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
from beanmachine.ppl.experimental.gp import (
... | beanmachine-main | tests/ppl/experimental/gp/models_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import unittest
import beanmachine.ppl as bm
import gpytorch
import torch
from beanmachine.ppl.experimental.gp import (
bm_... | beanmachine-main | tests/ppl/experimental/gp/inference_test.py |
beanmachine-main | tests/ppl/experimental/gp/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch.distributions as dist
from beanmachine.ppl.world.utils import get_default_transforms, initialize_value
def test... | beanmachine-main | tests/ppl/world/utils_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
from beanmachine.ppl.world import World
class SampleModel:
... | beanmachine-main | tests/ppl/world/world_test.py |
beanmachine-main | tests/ppl/world/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
import torch.distributions as dist
from beanmachine.ppl.world.initialize_fn import init_from_prior, init_to_unifo... | beanmachine-main | tests/ppl/world/initialize_fn_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
import torch.distributions as dist
from beanmachine.ppl.world.variable import Variable
def test_log_prob():
... | beanmachine-main | tests/ppl/world/variable_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from beanmachine.ppl.utils import tensorops
class TensorOpsTest(unittest.TestCase):
def test_gradients(s... | beanmachine-main | tests/ppl/utils/tensorops_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from beanmachine.ppl.utils.set_of_tensors import SetOfTensors
from torch import tensor
class SetOfTensorsTest(unittest.Te... | beanmachine-main | tests/ppl/utils/set_of_tensors_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from beanmachine.ppl.utils.multidictionary import MultiDictionary
class MultiDictionaryTest(unittest.TestCase):
def t... | beanmachine-main | tests/ppl/utils/multidictionary_test.py |
beanmachine-main | tests/ppl/utils/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for Graph from graph.py"""
import unittest
from beanmachine.ppl.utils.graph import Graph
class SimpleNode(object):
name: str... | beanmachine-main | tests/ppl/utils/graph_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for item_counter.py"""
import unittest
from beanmachine.ppl.utils.item_counter import ItemCounter
class ItemCounterTest(unittest... | beanmachine-main | tests/ppl/utils/item_counter_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for print_tree from treeprinter.py"""
import unittest
from beanmachine.ppl.utils.treeprinter import print_tree
class TreePrinter... | beanmachine-main | tests/ppl/utils/treeprinter_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for memoize.py"""
import unittest
from beanmachine.ppl.utils.memoize import memoize
count1 = 0
def fib(n):
global count1
... | beanmachine-main | tests/ppl/utils/memoize_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for partition functions from equivalence.py"""
import unittest
from typing import Any, Iterable
from beanmachine.ppl.utils.equival... | beanmachine-main | tests/ppl/utils/equivalence_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for print_graph from dotbuilder.py"""
import unittest
from typing import Any, Dict
from beanmachine.ppl.utils.dotbuilder import Do... | beanmachine-main | tests/ppl/utils/dotbuilder_test.py |
beanmachine-main | tests/ppl/testlib/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Tests for hypothesis_testing.py"""
import unittest
from beanmachine.ppl.testlib.hypothesis_testing import (
inverse_chi2_cdf,
in... | beanmachine-main | tests/ppl/testlib/hypothesis_testing_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pickle
import unittest
import warnings
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
import torch.uti... | beanmachine-main | tests/ppl/model/rv_identifier_test.py |
beanmachine-main | tests/ppl/model/__init__.py | |
beanmachine-main | tests/ppl/diagnostics/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from typing import Dict
import beanmachine.ppl as bm
import beanmachine.ppl.diagnostics.common_statistics as common_statist... | beanmachine-main | tests/ppl/diagnostics/diagnostics_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_newtonian_monte_carlo_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pickle
import unittest
import beanmachine.ppl as bm
import numpy as np
import torch
import torch.distributions as dist
import xarray... | beanmachine-main | tests/ppl/inference/monte_carlo_samples_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_uniform_mh_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_random_walk_adaptive_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
class SampleModel:
@bm.random_variable
def foo(self):... | beanmachine-main | tests/ppl/inference/single_site_ancestral_mh_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_no_u_turn_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
@bm.random_variable
def foo():
return dist.Normal(0.0, 1.... | beanmachine-main | tests/ppl/inference/utils_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import beanmachine.ppl as bm
import pytest
import torch
import torch.distributions as dist
class SampleModel:
@bm.ran... | beanmachine-main | tests/ppl/inference/nnc_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import sys
import beanmachine.ppl as bm
import pytest
import torch
import torch.distributions as dist
from beanmachine.ppl.infe... | beanmachine-main | tests/ppl/inference/inference_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
class SingleSiteUniformMetropolisHastingsTes... | beanmachine-main | tests/ppl/inference/single_site_uniform_mh_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
from typing import Optional
import beanmachine.ppl as bm
import numpy
import pytest
import scipy.stats
import torch
import... | beanmachine-main | tests/ppl/inference/vi_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import beanmachine.ppl as bm
import pytest
import torch
import torch.distributions as dist
from beanmachine.ppl... | beanmachine-main | tests/ppl/inference/vi_gpu_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
from beanmachine.ppl.examples.conjugate_models... | beanmachine-main | tests/ppl/inference/single_site_random_walk_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
class IntegrationTest(unittest.TestCase):
... | beanmachine-main | tests/ppl/inference/inference_integration_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import Counter
from unittest.mock import patch
import beanmachine.ppl as bm
import pytest
import torch
import torch.distri... | beanmachine-main | tests/ppl/inference/compositional_infer_test.py |
beanmachine-main | tests/ppl/inference/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
class SampleModel:
@bm.random_variable
def foo(self):... | beanmachine-main | tests/ppl/inference/sampler_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_random_walk_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import beanmachine.ppl as bm
import pytest
import torch
import torch.distributions as dist
@bm.random_variable
def f():
... | beanmachine-main | tests/ppl/inference/inference_error_reporting_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
class PredictiveTest(unittest.TestCase):
... | beanmachine-main | tests/ppl/inference/predictive_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from sys import float_info
import torch.distributions as dist
from beanmachine.ppl.testlib.hypothesis_testing import (
... | beanmachine-main | tests/ppl/inference/hypothesis_testing_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
import torch
import torch.distributions as dist
from beanmachine.ppl.inference import SingleS... | beanmachine-main | tests/ppl/inference/single_site_newtonian_monte_carlo_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_hamiltonian_monte_carlo_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import beanmachine.ppl as bm
from beanmachine.ppl.testlib.abstract_conjugate import AbstractConjugateTests
class SingleSi... | beanmachine-main | tests/ppl/inference/single_site_ancestral_mh_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from beanmachine.ppl.inference.compositional_infer import CompositionalInference
from beanmachine.ppl.testlib.abstract_conj... | beanmachine-main | tests/ppl/inference/compositional_infer_conjugate_test_nightly.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import pytest
import torch
import torch.distributions as dist
from beanmachine.ppl.inference.proposer.hmc_propo... | beanmachine-main | tests/ppl/inference/proposer/hmc_proposer_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Unit test for NormalEig class"""
import unittest
import torch
from beanmachine.ppl.inference.proposer.normal_eig import NormalEig
from t... | beanmachine-main | tests/ppl/inference/proposer/normal_eig_test.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from beanmachine.ppl.inference.proposer.utils import DictToVecConverter
def test_dict_to_vec_conversion():
d = {"a": torc... | beanmachine-main | tests/ppl/inference/proposer/utils_test.py |
beanmachine-main | tests/ppl/inference/proposer/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import beanmachine.ppl as bm
import pytest
import torch
import torch.distributions as dist
from beanmachine.ppl.inference.proposer.nuts_prop... | beanmachine-main | tests/ppl/inference/proposer/nuts_proposer_test.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.