python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys
from aepsych.config import Config
from .min_asks import MinAsks
from .min_total_outcome_... | aepsych-main | aepsych/generators/completion_criterion/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict
from aepsych.config import Config, ConfigurableMixin
from ax.modelbrid... | aepsych-main | aepsych/generators/completion_criterion/min_total_outcome_occurrences.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict
from aepsych.config import Config, ConfigurableMixin
from ax.core.base_... | aepsych-main | aepsych/generators/completion_criterion/min_total_tells.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys
from ..config import Config
from .factory import (
default_mean_covar_factory,
mo... | aepsych-main | aepsych/factory/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from configparser import NoOptionError
from typing import Optional, Tuple
import gpytorch
import tor... | aepsych-main | aepsych/factory/factory.py |
#!/usr/bin/env python3
# coding: utf-8
# ### Semi-parametric psychophysical model tutorial
#
# This tutorial will demonstrate how to fit the semiparametric psychophysical models described in [Keeley et al., 2023](https://arxiv.org/abs/2302.01187).
#
# The semi-parametric model uses a conventional parametric form fo... | aepsych-main | website/static/files/Semi_P_tutorial.py |
#!/usr/bin/env python3
# coding: utf-8
# # Data Collection and Analysis Using AEPsych
#
# This tutorial serves as a complete example on how to collect and analyze data from perceptual experiments using AEPsych. For more information on AEPsych, refer to the documentation in the [GitHub repository](https://github.com/f... | aepsych-main | website/static/files/data_collection_analysis_tutorial.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from copy import deepcopy
import numpy as np
import torch
from aepsych.config import... | aepsych-main | tests/test_utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from unittest.mock import MagicMock
import numpy as np
import torch
from aepsych.acq... | aepsych-main | tests/test_strategy.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from aepsych.benchmark.test_functions import make_songetal_testfun
from... | aepsych-main | tests/test_bench_testfuns.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import shutil
import unittest
import uuid
from configparser import DuplicateOpt... | aepsych-main | tests/test_db.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from itertools import product
import numpy as np
import torch
from aepsych.acquisiti... | aepsych-main | tests/test_lookahead.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| aepsych-main | tests/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import gpytorch
import numpy as np
from aepsych.config import Config
from aepsych.fa... | aepsych-main | tests/test_mean_covar_factories.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from scipy.stats import norm
def f_1d(x, mu=0):
"""
latent is just a gau... | aepsych-main | tests/common.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import random
import time
import unittest
import numpy as np
import torch
from aepsych.ben... | aepsych-main | tests/test_benchmark.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import unittest
import uuid
import torch
from aepsych.acquisition import EAVC,... | aepsych-main | tests/test_config.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from aepsych.likelihoods import OrdinalLikelihood
from gpytorch.test.ba... | aepsych-main | tests/test_likelihoods.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import uuid
import torch
from aepsych.server import AEPsychServer
from ae... | aepsych-main | tests/test_multioutcome.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import os
import unittest
import uuid
import numpy as np
import torch
from aepsych.conf... | aepsych-main | tests/test_ax_integration.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
These tests check that the server can handle different experiments
(multi/single stimuli, multi/s... | aepsych-main | tests/test_integration.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| aepsych-main | tests/acquisition/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from aepsych.acquisition.lse import MCLevelSetEstima... | aepsych-main | tests/acquisition/test_lse.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from aepsych.acquisition.mutual_information import (... | aepsych-main | tests/acquisition/test_mi.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from itertools import product
import numpy as np
import torch
from aepsych.acquisiti... | aepsych-main | tests/acquisition/test_objective.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from aepsych.acquisition.monotonic_rejection import MonotonicMCLSE
from aepsych.acquisit... | aepsych-main | tests/acquisition/test_monotonic.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from aepsych.acquisition.rejection_sampler import RejectionSampler
from aepsych.models.d... | aepsych-main | tests/acquisition/test_rejection_sampler.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| aepsych-main | tests/server/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import select
import unittest
import uuid
from unittest.mock import MagicM... | aepsych-main | tests/server/test_server.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from aepsych.config import Config
from ..test_server import BaseServerTestCase, dum... | aepsych-main | tests/server/message_handlers/test_handle_get_config.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| aepsych-main | tests/server/message_handlers/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from unittest.mock import MagicMock
from ..test_server import BaseServerTestCase
c... | aepsych-main | tests/server/message_handlers/test_handle_exit.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from ..test_server import BaseServerTestCase, dummy_config
class QueryHandlerTestC... | aepsych-main | tests/server/message_handlers/test_query_handlers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from ..test_server import BaseServerTestCase, dummy_config
class StratCanModelTest... | aepsych-main | tests/server/message_handlers/test_can_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from ..test_server import BaseServerTestCase, dummy_config
class ResumeTestCase(Ba... | aepsych-main | tests/server/message_handlers/test_handle_finish_strategy.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from unittest.mock import MagicMock
from ..test_server import BaseServerTestCase, du... | aepsych-main | tests/server/message_handlers/test_tell_handlers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from sklearn.datasets import make_classification
fr... | aepsych-main | tests/models/test_utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import torch
# run on single threads to keep us from deadlocking weirdly ... | aepsych-main | tests/models/test_monotonic_projection_gp.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import numpy as np
import torch
from aepsych.generators import SobolGenera... | aepsych-main | tests/models/test_multitask_regression.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import uuid
import numpy as np
import numpy.testing as npt
import torch
fr... | aepsych-main | tests/models/test_gp_regression.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| aepsych-main | tests/models/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import numpy.testing as npt
import torch
from aepsych.acquisitio... | aepsych-main | tests/models/test_semi_p.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from aepsych.kernels.rbf_partial_grad import RBFKernelPartialObsGrad
from aepsych.means.... | aepsych-main | tests/models/test_derivative_gp.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import numpy.testing as npt
import torch
from aepsych.likelihoods... | aepsych-main | tests/models/test_variational_gp.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
import unittest
import uuid
import numpy as np
import numpy.testing as npt
import tor... | aepsych-main | tests/models/test_pairwise_probit.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import torch
# run on single threads to keep us from deadlocking weirdly in CI
if "CI" in... | aepsych-main | tests/models/test_monotonic_rejection_gp.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from botorch.fit import fit_gpytorch_mll
from gpytor... | aepsych-main | tests/models/test_model_query.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import torch
# run on single threads to keep us from deadlocking weirdly ... | aepsych-main | tests/models/test_gp_classification.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import time
import unittest
import numpy as np
import torch
from aepsych.acquisition import MCLevelS... | aepsych-main | tests/generators/test_optimize_acqf_generator.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import numpy.testing as npt
from aepsych.config import Config
fro... | aepsych-main | tests/generators/test_random_generator.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from aepsych.config import Config
from aepsych.generators.completion_criterion impor... | aepsych-main | tests/generators/test_completion_criteria.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from unittest.mock import MagicMock
import numpy as np
import torch
from aepsych.acq... | aepsych-main | tests/generators/test_epsilon_greedy_generator.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| aepsych-main | tests/generators/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import numpy.testing as npt
import torch
from aepsych.config impo... | aepsych-main | tests/generators/test_sobol_generator.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import numpy.testing as npt
from aepsych.config import Config
fro... | aepsych-main | tests/generators/test_manual_generator.py |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# Th... | aepsych-main | sphinx/source/conf.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from datetime import datetime
import numpy as np
constants = {
"savefolder": "./databases/",
... | aepsych-main | examples/contrast_discrimination_psychopy/experiment_config.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import experiment_config
import numpy as np
import torch
from aepsych_client import AEPsychClient
fro... | aepsych-main | examples/contrast_discrimination_psychopy/experiment.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import pyglet
from psychopy import core, event
from psychopy.visual import Window
... | aepsych-main | examples/contrast_discrimination_psychopy/helpers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# we have pretty verbose messaging by default, suppress that here
import logging
import warnings
war... | aepsych-main | pubs/owenetal/code/benchmark_threshold.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from copy import copy
import matplotlib.pyplot as plt
import numpy as np
import torch
from aepsych.... | aepsych-main | pubs/owenetal/code/stratplots.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import gpytorch
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch... | aepsych-main | pubs/owenetal/code/prior_plots.py |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the scripts directory.
from __future__ import annotations
import argparse
import json
import os
import nbformat
from bs4 import BeautifulSoup
from nbconver... | aepsych-main | scripts/parse_tutorials.py |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the scripts directory.
from __future__ import annotations
import argparse
import os
from bs4 import BeautifulSoup
#The base_url must match the base url in... | aepsych-main | scripts/parse_sphinx.py |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved.
from __future__ import annotations
import argparse
import json
import os
import shutil
import nbformat
from bs4 import BeautifulSoup
from nbconvert import HTMLExporter
TEMPLATE = """const CWD = process.cwd();
const Rea... | aepsych-main | scripts/parse_demos.py |
from setuptools import find_packages, setup
from os.path import basename, splitext
from glob import glob
setup(name='smallfry',
version='0.1',
description='Code for smallfry.',
packages=find_packages("src"),
package_dir={"": "src"},
py_modules=[splitext(basename(path))[0] for path in glob... | smallfry-master | setup.py |
from quant_embedding import compress_long_mat
from quant_embedding import decompress_long_mat
from quant_embedding import QuantEmbedding
from quant_embedding import quantize_embed
import compress
from unittest import TestCase
import torch
import numpy as np
import logging
import sys
logging.basicConfig(stream=sys.stdou... | smallfry-master | src/smallfry/quant_embedding_test.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from smallfry import compress
import logging
import sys, os
LONG_BITS = 64
def fix_randomness(seed):
np.random.seed(seed)
use_cuda = torch.cuda.is_available()
torch.manual_seed(seed)
if use_cuda:
... | smallfry-master | src/smallfry/quant_embedding.py |
import os
import sys
import socket
import json
import datetime
import logging
import pathlib
import time
import random
import subprocess
import argparse
import numpy as np
import getpass
def load_embeddings(path):
"""
Loads a GloVe or FastText format embedding at specified path. Returns a
vector of string... | smallfry-master | src/smallfry/utils.py |
import os
import logging
import math
import time
import pathlib
import traceback
import numpy as np
from smallfry import utils
def compress_uniform(X, bit_rate, adaptive_range=False, stochastic_round=False,
skip_quantize=False):
'''
This function compresses an embedding matrix using uniform quantizatio... | smallfry-master | src/smallfry/compress.py |
from utils import *
import pickle as pkl
import datetime, os
class ModelParams:
def __init__(self, dataset_name, transform, test, log_path, input_size,
layer_size, out_size, num_layers, loss, r, steps, batch_size,
lr, mom, init_type, class_type, learn_corner, n_diag_learned,
ini... | structured-nets-master | tensorflow/model_params.py |
import tensorflow as tf
from utils import *
import numpy as np
from scipy.linalg import solve_sylvester
import time
from krylov import *
def eigendecomp(A):
d, P = tf.self_adjoint_eig(A)
return P, tf.diag(d), tf.matrix_inverse(P)
def general_recon(G, H, A, B):
P,D_A, Pinv = eigendecomp(A)
Q, D_B, Qin... | structured-nets-master | tensorflow/reconstruction.py |
import numpy as np
import os
import tensorflow as tf
from utils import *
from reconstruction import *
from model import *
def vandermonde_like(dataset, params, test_freq=100, verbose=False):
# A is learned, B is fixed
B_vand = gen_Z_f(params.layer_size, 0).T
f_V = 0
# Create the model
x = tf.placeholder(tf.float... | structured-nets-master | tensorflow/fixed_operators.py |
import tensorflow as tf
import io,os
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, '../tensorflow/')
from utils import *
from PIL import Image
import numpy as np
import time
# One image with params.num_pred_plot: image, caption - actual and predicted
# Here assum... | structured-nets-master | tensorflow/visualize.py |
from scipy.sparse import diags
import numpy as np
import tensorflow as tf
import functools
from reconstruction import *
from utils import *
from krylov import *
def check_rank(sess, x, y_, batch_xs, batch_ys, params, model):
if not params.check_disp:
return
if params.class_type in ['unconstrained', '... | structured-nets-master | tensorflow/model.py |
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import os,sys,h5py
import scipy.io as sio
from scipy.linalg import solve_sylvester
import pickle as pkl
from sklearn.preprocessing import OneHotEncoder
# sys.path.insert(0, '../../../../')
from utils import *
class Dataset:
# here n is t... | structured-nets-master | tensorflow/dataset.py |
from scipy.linalg import toeplitz, circulant, solve_sylvester
from scipy.sparse import diags
import numpy as np
import tensorflow as tf
import time, subprocess
import functools
def kth_diag_indices(A, k):
rows, cols = np.diag_indices_from(A)
if k < 0:
return rows[-k:], cols[:k]
elif k > 0:
return rows[:-k], col... | structured-nets-master | tensorflow/utils.py |
import numpy as np
from scipy.sparse import diags
import tensorflow as tf
import functools
def identity_mult_fn(v, n):
return v
# Multiplication by (Z_{f,v} + diag(d))^T
def circ_diag_transpose_mult_fn(v_f, d, x, n):
#sess = tf.InteractiveSession()
#tf.initialize_all_variables().run()
#print sess.ru... | structured-nets-master | tensorflow/krylov.py |
import numpy as np
import tensorflow as tf
from utils import *
from reconstruction import *
from krylov import *
import functools
import time, os
# Only an approximate reconstruction.
def tridiagonal_corner(dataset, params, test_freq=100, verbose=False):
# Create the model
x = tf.placeholder(tf.float64, [None, param... | structured-nets-master | tensorflow/learned_operators.py |
"""
Compare methods in parallel, spawning separate thread for each.
"""
import sys, os, datetime
import pickle as pkl
sys.path.insert(0, '../../')
# from optimize import optimize
from utils import *
from model_params import ModelParams
from dataset import Dataset
import argparse
import thread
def create_command(args,... | structured-nets-master | tensorflow/compare_parallel.py |
import numpy as np
import os
#os.environ["CUDA_VISIBLE_DEVICES"]="-1"
import tensorflow as tf
import pickle as pkl
from utils import *
from reconstruction import *
from visualize import visualize
from model import *
import time
import logging
def restore_from_checkpoint(dataset, params, sess, saver, x, y_, loss, accur... | structured-nets-master | tensorflow/optimize_tf.py |
"""
Compare methods and hyperparameter settings sequentially.
"""
import sys, os, datetime
import pickle as pkl
# sys.path.insert(0, '../')
import argparse
import threading
import logging
import numpy as np
from optimize_tf import optimize_tf
from utils import *
from model_params import ModelParams
from dataset impor... | structured-nets-master | tensorflow/compare.py |
import sys
sys.path.insert(0, '../')
from reconstruction import *
from model_params import ModelParams
from utils import *
from krylov import *
from scipy.linalg import toeplitz
import numpy as np
def test_circ_sparsity(n):
# Generate Toeplitz matrix
A = gen_Z_f(n, 1).T
B = gen_Z_f(n, -1)
M = toeplitz(np.random.r... | structured-nets-master | tensorflow/tests/test_reconstruction.py |
"""
Computes projections onto various classes.
"""
import numpy as np
from scipy.linalg import toeplitz
def kth_diag_indices(A, k):
rows, cols = np.diag_indices_from(A)
if k < 0:
return rows[-k:], cols[:k]
elif k > 0:
return rows[:-k], cols[k:]
else:
return rows, cols
# Project... | structured-nets-master | scripts/misc/projections.py |
import pickle
import numpy as np
import matplotlib.pyplot as plt
arrays = pickle.load(open('mnist_noise_toep_dr2_0.pkl', 'rb'), encoding='bytes')
G_toeplitz, H_toeplitz, W_toeplitz = [arrays[key] for key in [b'G', b'H', b'W']]
arrays = pickle.load(open('mnist_noise_circ_0.pkl', 'rb'), encoding='bytes')
A_subdiag, B_su... | structured-nets-master | scripts/analysis/analysis.py |
import matplotlib.pyplot as plt
import numpy as np
# from matplotlib import rc
# # activate latex text rendering
# rc('text', usetex=True)
def update_minmax(mini, maxi, a):
return min(mini, min(a)), max(maxi, max(a))
def normalize(params, n):
return [float(p)/n**2 for p in params]
# return [n**2/float(p)... | structured-nets-master | scripts/visualizations/acc_vs_params.py |
import matplotlib.pyplot as plt
import pickle as pkl
import numpy as np
n_iters = 20000
step = 100
n = 50
mom = 0.99
prefix = '../../results/mom'
xs = np.arange(0, n_iters, step)
trid_corner = pkl.load(open(prefix + str(mom) + '_' + 'toeplitz_tridiagonal_corner_losses_' + str(n) + '.p', 'rb'))
circ = pkl.load(open(pr... | structured-nets-master | scripts/visualizations/make_plot.py |
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pickle as pkl
# MNIST variants
# idx = 1
# # data_loc = '/dfs/scratch1/thomasat/datasets/mnist_noise/mnist_noise_variations_all_' + str(idx) + '.amat'
# data_loc = '/dfs/scratch1/thomasat/datasets/mnist_bg_rot/mnist_all_... | structured-nets-master | scripts/visualizations/show_example.py |
import os
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"
import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from timeit import default_timer as timer
import timeit
import pickle as pkl
import matplotlib.patches as mpatches
impor... | structured-nets-master | scripts/visualizations/plot_speed.py |
import matplotlib.pyplot as plt
# For MNIST noise
fixed_xs = [10202, 11770, 13338, 14906]
lowrank = [0.2403, 0.377, 0.4577, 0.546]
lowrank_std = [0.0184407, 0.004, 0.00334066, 0.006]
toep = [0.62525, 0.681, 0.6758, 0.712]
toep_std = [0.00125, 0.017, 0.0227389, 0.012]
hank = [0.66175, 0.696667, 0.70475, 0.704]
hank_std... | structured-nets-master | scripts/visualizations/make_params_plot_iclr.py |
import numpy as np
import matplotlib.pyplot as plt
# name = 'mnist_sd_r4'
names = ['bgrot_sd_r1', 'bgrot_sd_r4', 'bgrot_sd_r16']
# names = ['patch2_sd_r8', 'patch_sd_r8_best']
ranks = [1, 1, 4, 4, 16, 16]
for name in names:
# for name in [names[2]]:
n = 1024
# r = 4
G = np.loadtxt(name+'_G')
H = np.lo... | structured-nets-master | scripts/visualizations/fat.py |
# Modified from https://github.com/ndrplz/small_norb/blob/master/smallnorb/dataset.py
import struct
import numpy as np
#import matplotlib.pyplot as plt
import scipy.misc
from tqdm import tqdm
from os import makedirs
from os.path import join
from os.path import exists
from itertools import groupby
#names = ['train1', ... | structured-nets-master | scripts/data/norb.py |
import numpy as np
import pickle as pkl
from sklearn.preprocessing import OneHotEncoder
from data_utils import normalize_data, apply_normalization
# Download from http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DeepVsShallowComparisonICML2007
def process_data(data):
X = data[:, :-1]
Y = np.expand_... | structured-nets-master | scripts/data/preprocess_convex.py |
import numpy as np
import pickle as pkl
import os
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
from data_utils import normalize_data, apply_normalization
# Download from https://www.cs.toronto.edu/~kriz/cifar.html
# Assumes 3 input channels
# Converts to grayscale
def convert_graysc... | structured-nets-master | scripts/data/preprocess_cifar10.py |
import h5py
import scipy.io as sio
import numpy as np
import pickle as pkl
def process(feat_loc,lab_loc,train,top_N_classes=None,N=None):
lab = sio.loadmat(lab_loc)['lab']
if train:
with h5py.File(feat_loc) as f:
feat = np.array(f['fea'])
else:
feat = sio.loadmat(feat_loc)['fea']
if top_N_classes is None:
... | structured-nets-master | scripts/data/timit.py |
# Download from https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
import sys
import pickle as pkl
sys.path.insert(0, '../../')
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from smallnorb import SmallNORBDataset
from scipy.misc import imresize
MAX_VAL = 255.0
DS_SIZE = (24, 24)
N_CATEGORIES = 5
OU... | structured-nets-master | scripts/data/preprocess_smallnorb.py |
# Download from http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DeepVsShallowComparisonICML2007
import numpy as np
import pickle as pkl
from sklearn.preprocessing import OneHotEncoder
from data_utils import normalize_data, apply_normalization
def process_data(data):
X = data[:, :-1]
Y = np.expand_... | structured-nets-master | scripts/data/preprocess_rect.py |
import numpy as np
def normalize_data(data):
mean = np.mean(data,axis=0)
std = np.std(data,axis=0)
return apply_normalization(data,mean,std), mean, std
def apply_normalization(data, mean, std):
normalized = (data-mean)/std
print('Apply normalization: mean, std: ', np.mean(normalized,axis=0), np.s... | structured-nets-master | scripts/data/data_utils.py |
# From https://github.com/ndrplz/small_norb/blob/master/smallnorb/dataset.py
import struct
import numpy as np
import matplotlib.pyplot as plt
import scipy.misc
from tqdm import tqdm
from os import makedirs
from os.path import join
from os.path import exists
from itertools import groupby
class SmallNORBExample:
... | structured-nets-master | scripts/data/smallnorb.py |
import numpy as np
import pickle as pkl
from sklearn.preprocessing import OneHotEncoder
from data_utils import normalize_data, apply_normalization
# Download from http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DeepVsShallowComparisonICML2007
n_variations = 6
for idx in np.arange(1, n_variations+1):
d... | structured-nets-master | scripts/data/preprocess_mnist_noise.py |
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