python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
import logging
from pathlib2 import Path
from memcnn.utils.log import setup, SummaryWriter
def test_setup(tmp_path):
logfile = str(tmp_path / 'testlog.log')
setup(use_stdout=True, filename=logfile, log_level=logging.DEBUG)
def test_summary_writer(tmp_path):
logfile = Path(tmp_path / 'scalars.json')
... | memcnn-master | memcnn/utils/tests/test_log.py |
import torch
from memcnn.utils.loss import _assert_no_grad, CrossEntropyLossTF
def test_assert_no_grad():
data = torch.ones(3, 3, 3)
data.requires_grad = False
_assert_no_grad(data)
def test_crossentropy_tf():
batch_size = 5
shape = (batch_size, 2)
loss = CrossEntropyLossTF()
ypred = tor... | memcnn-master | memcnn/utils/tests/test_loss.py |
# -*- coding: utf-8 -*-
from functools import partial
import numpy as np
import torch
import torch.nn as nn
import warnings
from memcnn.models.additive import AdditiveCoupling
from memcnn.models.affine import AffineCoupling
from memcnn.models.utils import pytorch_version_one_and_above
warnings.filterwarnings(action='... | memcnn-master | memcnn/models/revop.py |
import torch
import torch.nn as nn
import copy
import warnings
from torch import set_grad_enabled
warnings.filterwarnings(action='ignore', category=UserWarning)
class AffineAdapterNaive(nn.Module):
""" Naive Affine adapter
Outputs exp(f(x)), f(x) given f(.) and x
"""
def __init__(self, module):
... | memcnn-master | memcnn/models/affine.py |
memcnn-master | memcnn/models/__init__.py | |
"""ResNet/RevNet implementation used for The Reversible Residual Network
Implemented in PyTorch instead of TensorFlow.
@inproceedings{gomez17revnet,
author = {Aidan N. Gomez and Mengye Ren and Raquel Urtasun and Roger B. Grosse},
title = {The Reversible Residual Network: Backpropagation without Storing Acti... | memcnn-master | memcnn/models/resnet.py |
import torch
# for backwards compatibility
use_context_mans = True
try:
pytorch_version_one_and_above = int(torch.__version__[0]) > 0
except TypeError:
pytorch_version_one_and_above = True
| memcnn-master | memcnn/models/utils.py |
import warnings
import torch
import torch.nn as nn
import copy
from torch import set_grad_enabled
class AdditiveCoupling(nn.Module):
def __init__(self, Fm, Gm=None, implementation_fwd=-1, implementation_bwd=-1):
"""
This computes the output :math:`y` on forward given input :math:`x` and arbitrary ... | memcnn-master | memcnn/models/additive.py |
import pytest
import torch
from memcnn.models.resnet import ResNet, BasicBlock, Bottleneck, RevBasicBlock, RevBottleneck
@pytest.mark.parametrize('block,batch_norm_fix', [(BasicBlock, True), (Bottleneck, False), (RevBasicBlock, False), (RevBottleneck, True)])
def test_resnet(block, batch_norm_fix):
model = ResNet... | memcnn-master | memcnn/models/tests/test_resnet.py |
memcnn-master | memcnn/models/tests/__init__.py | |
import pytest
import gc
import numpy as np
import math
from collections import defaultdict
import torch
import torch.nn
from memcnn.models.tests.test_revop import SubModuleStack, SubModule
def readable_size(num_bytes):
return '{:.2f}'.format(float(num_bytes) / float(1024 ** 2))
LEN = 79
# some pytorch low-leve... | memcnn-master | memcnn/models/tests/test_memory_saving.py |
import torch
import torch.nn
import pytest
import copy
import warnings
from memcnn import create_coupling, InvertibleModuleWrapper
from memcnn.models.tests.test_revop import set_seeds, SubModule
from memcnn.models.affine import AffineAdapterNaive, AffineBlock
from memcnn.models.additive import AdditiveBlock
@pytest.... | memcnn-master | memcnn/models/tests/test_couplings.py |
import warnings
import pytest
import random
import torch
import torch.nn
import numpy as np
import copy
from memcnn.models.affine import AffineAdapterNaive, AffineAdapterSigmoid, AffineCoupling
from memcnn import ReversibleBlock
from memcnn.models.revop import InvertibleModuleWrapper, create_coupling, is_invertible_mod... | memcnn-master | memcnn/models/tests/test_revop.py |
import time
import logging
import torch
import numpy as np
from memcnn.utils.stats import AverageMeter, accuracy
from memcnn.utils.log import SummaryWriter
logger = logging.getLogger('trainer')
def validate(model, ceriterion, val_loader, device):
"""validation sub-loop"""
model.eval()
batch_time = Avera... | memcnn-master | memcnn/trainers/classification.py |
memcnn-master | memcnn/trainers/__init__.py | |
import json
import pytest
import os
import sys
import torch
from memcnn.experiment.manager import ExperimentManager
from memcnn.train import run_experiment, main
try:
from pathlib2 import Path
except ImportError:
from pathlib import Path
def test_main(tmp_path):
sys.argv = ['train.py', 'cifar10', 'resne... | memcnn-master | memcnn/trainers/tests/test_train.py |
memcnn-master | memcnn/trainers/tests/__init__.py | |
import pytest
from memcnn.trainers.classification import train
from memcnn.experiment.manager import ExperimentManager
from memcnn.data.cifar import get_cifar_data_loaders
from memcnn.utils.loss import CrossEntropyLossTF
import torch
from torchvision.datasets.cifar import CIFAR10
class SimpleTestingModel(torch.nn.Mo... | memcnn-master | memcnn/trainers/tests/test_classification.py |
import torch
import torch.nn as nn
import memcnn
# define a new torch Module with a sequence of operations: Relu o BatchNorm2d o Conv2d
class ExampleOperation(nn.Module):
def __init__(self, channels):
super(ExampleOperation, self).__init__()
self.seq = nn.Sequential(
... | memcnn-master | memcnn/examples/minimal.py |
import torch
import sys
def test_minimal():
import minimal
# Input and inversed output should be approximately the same
assert torch.allclose(minimal.X, minimal.X2, atol=1e-06)
# Output of the wrapped invertible module is unlikely to match the normal output of F
assert not torch.allclose(minimal.... | memcnn-master | memcnn/examples/test_examples.py |
memcnn-master | memcnn/experiment/__init__.py | |
import json
import copy
def get_attr_from_module(pclass):
pclass = pclass.rsplit(".", 1)
mod = __import__(pclass[0], fromlist=[str(pclass[1])])
return getattr(mod, pclass[1])
def load_experiment_config(experiments_file, experiment_tags):
with open(experiments_file, 'r') as f:
data = json.loa... | memcnn-master | memcnn/experiment/factory.py |
import os
import glob
import torch
import logging
import shutil
import numpy as np
class ExperimentManager(object):
def __init__(self, experiment_dir, model=None, optimizer=None):
self.logger = logging.getLogger(type(self).__name__)
self.experiment_dir = experiment_dir
self.model = model
... | memcnn-master | memcnn/experiment/manager.py |
import pytest
import os
import memcnn.experiment.factory
from memcnn.config import Config
def test_get_attr_from_module():
a = memcnn.experiment.factory.get_attr_from_module('memcnn.experiment.factory.get_attr_from_module')
assert a is memcnn.experiment.factory.get_attr_from_module
def test_load_experiment_... | memcnn-master | memcnn/experiment/tests/test_factory.py |
memcnn-master | memcnn/experiment/tests/__init__.py | |
from memcnn.experiment.manager import ExperimentManager
import torch.nn
def test_experiment_manager(tmp_path):
exp_dir = tmp_path / "test_exp_dir"
man = ExperimentManager(str(exp_dir))
assert man.model is None
assert man.optimizer is None
man.make_dirs()
assert exp_dir.exists()
assert (ex... | memcnn-master | memcnn/experiment/tests/test_manager.py |
memcnn-master | memcnn/data/__init__.py | |
import torch
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import numpy as np
from memcnn.data.sampling import NSamplesRandomSampler
def random_crop_transform(x, crop_size=3, img_size=(32, 32)):
cz = (crop_size + 1) // 2
x_pad = np.pad(x, ((cz, cz), (cz, cz), (0, 0)), mod... | memcnn-master | memcnn/data/cifar.py |
import torch
from torch.utils.data.sampler import Sampler
class NSamplesRandomSampler(Sampler):
"""Samples elements randomly, with replacement,
always in blocks all elements of the dataset.
Only the remainder will be sampled with less elements.
Arguments:
data_source (Dataset): dataset to sam... | memcnn-master | memcnn/data/sampling.py |
import pytest
from memcnn.data.cifar import get_cifar_data_loaders, random_crop_transform
import torch.utils.data as data
import numpy as np
from PIL import Image
@pytest.mark.parametrize('crop_size,img_size', [(4, (32, 32)), (0, (32, 32))])
def test_random_crop_transform(crop_size, img_size):
np.random.seed(42)
... | memcnn-master | memcnn/data/tests/test_cifar.py |
memcnn-master | memcnn/data/tests/__init__.py | |
import pytest
from memcnn.data.sampling import NSamplesRandomSampler
import torch.utils.data as data
import numpy as np
@pytest.mark.parametrize('nsamples,data_samples', [(1, 1), (14, 10), (10, 14), (5, 1), (1, 5), (0, 10),
(np.array(4, dtype=np.int64), 12),
... | memcnn-master | memcnn/data/tests/test_sampling.py |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# memcnn documentation build configuration file, created by
# sphinx-quickstart on Fri Jun 9 13:47:02 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# auto... | memcnn-master | docs/conf.py |
from setuptools import setup, find_packages
setup(
name = 'logavgexp-pytorch',
packages = find_packages(exclude=[]),
version = '0.0.6',
license='MIT',
description = 'LogAvgExp - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/logavgexp-pytorch... | logavgexp-torch-main | setup.py |
from logavgexp_pytorch.logavgexp_pytorch import logavgexp, LogAvgExp, LogAvgExp2D, LogAvgExp3D
| logavgexp-torch-main | logavgexp_pytorch/__init__.py |
import math
from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
from unfoldNd import unfoldNd
# helper functions
def exists(t):
return t is not None
def log(t, eps = 1e-20):
return torch.log(t + eps)
def cast_tuple(t, length = 1):
... | logavgexp-torch-main | logavgexp_pytorch/logavgexp_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'lie-transformer-pytorch',
packages = find_packages(),
version = '0.0.17',
license='MIT',
description = 'Lie Transformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/lie-transforme... | lie-transformer-pytorch-main | setup.py |
import torch
from lie_transformer_pytorch import LieTransformer
def test_transformer():
model = LieTransformer(
dim = 512,
depth = 1
)
feats = torch.randn(1, 64, 512)
coors = torch.randn(1, 64, 3)
mask = torch.ones(1, 64).bool()
out = model(feats, coors, mask = mask)
asser... | lie-transformer-pytorch-main | tests.py |
import torch
import torch.nn as nn
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# helpers
def sum_tuple(x, y, dim = 1):
x = list(x)
x[dim] += y[dim]
return tuple(x)
def subtract_tuple(x, y, dim = 1):
x = list(x)
x[dim] -= y[d... | lie-transformer-pytorch-main | lie_transformer_pytorch/reversible.py |
from math import pi
import torch
from functools import wraps
from torch import acos, atan2, cos, sin
from einops import rearrange, repeat
# constants
THRES = 7e-2
# helper functions
def exists(val):
return val is not None
def to(t):
return {'device': t.device, 'dtype': t.dtype}
def taylor(thres):
def ... | lie-transformer-pytorch-main | lie_transformer_pytorch/se3.py |
from lie_transformer_pytorch.lie_transformer_pytorch import LieTransformer
| lie-transformer-pytorch-main | lie_transformer_pytorch/__init__.py |
import math
from functools import partial
import torch
import torch.nn.functional as F
from torch import nn, einsum
from lie_transformer_pytorch.se3 import SE3
from einops import rearrange, repeat
from lie_transformer_pytorch.reversible import SequentialSequence, ReversibleSequence
# helpers
def exists(val):
r... | lie-transformer-pytorch-main | lie_transformer_pytorch/lie_transformer_pytorch.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import torch.autograd as autograd
import torch.nn as nn
from util import *
import torch.nn.utils.rnn as rnn_utils
import time
import numpy as np
import openprotein
... | openprotein-master | models.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import torch
import torch.utils.data
import h5py
from datetime import datetime
import PeptideBuilder
import Bio.PDB
import math
import numpy as np
import os
import ... | openprotein-master | util.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import torch
from util import encode_primary_string, get_structure_from_angles, write_to_pdb, \
calculate_dihedral_angles_over_minibatch
input_sequences = ["S... | openprotein-master | prediction.py |
from preprocessing import *
import torch
import argparse
print("------------------------")
print("--- OpenProtein v0.1 ---")
print("------------------------")
parser = argparse.ArgumentParser(description = "OpenProtein version 0.1")
parser.add_argument('--no_force_pre_processing_overwrite', dest='no_force_pre_proces... | openprotein-master | preprocessing_cli.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
from flask import Flask, request, jsonify
from flask_cors import CORS, cross_origin
import threading
app = Flask(__name__)
cors = CORS(app)
data = None
@app.route... | openprotein-master | dashboard.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import glob
import os.path
import os
import platform
import numpy as np
import h5py
from util import AA_ID_DICT, calculate_dihedral_angles, protein_id_to_str, get_s... | openprotein-master | preprocessing.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import argparse
import importlib
from dashboard import start_dashboard_server
from util import *
print("------------------------")
print("--- OpenProtein v0.1 ---... | openprotein-master | __main__.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
from util import *
import time
import torch.nn.utils.rnn as rnn_utils
import torch.nn as nn
class BaseModel(nn.Module):
def __init__(self, use_gpu, embedding_s... | openprotein-master | openprotein.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
from util import *
import torch.optim as optim
import requests
import json
import time
def train_model(data_set_identifier, model, train_loader, validation_loader,... | openprotein-master | training.py |
# This file is part of the TMHMM3 project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import torch
from torch.utils.data.dataset import Dataset
import numpy as np
import math
import random
from util import write_out
class TMDataset(Dataset):
def __i... | openprotein-master | experiments/tmhmm3/tm_util.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
import os
import pickle
from .tm_models import *
from .tm_util import *
from models import *
from training import train_model
from util import write_out, set_exper... | openprotein-master | experiments/tmhmm3/__init__.py |
# This file is part of the TMHMM3 project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
from enum import Enum
import torch.autograd as autograd
import torch.nn as nn
import openprotein
from experiments.tmhmm3.tm_util import *
from pytorchcrf.torchcrf impor... | openprotein-master | experiments/tmhmm3/tm_models.py |
# This file is part of the OpenProtein project.
#
# @author Jeppe Hallgren
#
# For license information, please see the LICENSE file in the root directory.
from preprocessing import process_raw_data
from models import *
from training import train_model
def run_experiment(parser, use_gpu):
# parse experiment spec... | openprotein-master | experiments/example/__init__.py |
"""
pNeRF algorithm for parallelized conversion from torsion (dihedral) angles to
Cartesian coordinates implemented with PyTorch.
Reference implementation in tensorflow by Mohammed AlQuraishi:
https://github.com/aqlaboratory/pnerf/blob/master/pnerf.py
Paper (preprint) by Mohammed AlQuraishi:
https://www.biorxiv... | openprotein-master | pnerf/pnerf.py |
from setuptools import setup, find_packages
setup(
name = 'point-transformer-pytorch',
packages = find_packages(),
version = '0.1.5',
license='MIT',
description = 'Point Transformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/point-trans... | point-transformer-pytorch-main | setup.py |
import torch
from torch import nn, einsum
from einops import repeat
# helpers
def exists(val):
return val is not None
def max_value(t):
return torch.finfo(t.dtype).max
def batched_index_select(values, indices, dim = 1):
value_dims = values.shape[(dim + 1):]
values_shape, indices_shape = map(lambda t... | point-transformer-pytorch-main | point_transformer_pytorch/point_transformer_pytorch.py |
import torch
from torch import nn, einsum
from einops import repeat, rearrange
# helpers
def exists(val):
return val is not None
def max_value(t):
return torch.finfo(t.dtype).max
def batched_index_select(values, indices, dim = 1):
value_dims = values.shape[(dim + 1):]
values_shape, indices_shape = m... | point-transformer-pytorch-main | point_transformer_pytorch/multihead_point_transformer_pytorch.py |
from point_transformer_pytorch.point_transformer_pytorch import PointTransformerLayer
from point_transformer_pytorch.multihead_point_transformer_pytorch import MultiheadPointTransformerLayer
| point-transformer-pytorch-main | point_transformer_pytorch/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'esbn-pytorch',
packages = find_packages(),
version = '0.0.4',
license='MIT',
description = 'Emergent Symbol Binding Network - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/ESBN-pytor... | ESBN-pytorch-main | setup.py |
from esbn_pytorch.esbn_pytorch import ESBN
| ESBN-pytorch-main | esbn_pytorch/__init__.py |
import torch
from functools import partial
from torch import nn, einsum
from einops import repeat, rearrange
# helpers
def exists(val):
return val is not None
def safe_cat(t, el, dim = 0):
if not exists(t):
return el
return torch.cat((t, el), dim = dim)
def map_fn(fn, *args, **kwargs):
def i... | ESBN-pytorch-main | esbn_pytorch/esbn_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'pixel-level-contrastive-learning',
packages = find_packages(),
version = '0.1.1',
license='MIT',
description = 'Pixel-Level Contrastive Learning',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains... | pixel-level-contrastive-learning-main | setup.py |
import math
import copy
import random
from functools import wraps, partial
from math import floor
import torch
from torch import nn, einsum
import torch.nn.functional as F
from kornia import augmentation as augs
from kornia import filters, color
from einops import rearrange
# helper functions
def identity(t):
... | pixel-level-contrastive-learning-main | pixel_level_contrastive_learning/pixel_level_contrastive_learning.py |
from pixel_level_contrastive_learning.pixel_level_contrastive_learning import PPM, PixelCL
| pixel-level-contrastive-learning-main | pixel_level_contrastive_learning/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'byol-pytorch',
packages = find_packages(exclude=['examples']),
version = '0.6.0',
license='MIT',
description = 'Self-supervised contrastive learning made simple',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://githu... | byol-pytorch-master | setup.py |
from byol_pytorch.byol_pytorch import BYOL
| byol-pytorch-master | byol_pytorch/__init__.py |
import copy
import random
from functools import wraps
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms as T
# helper functions
def default(val, def_val):
return def_val if val is None else val
def flatten(t):
return t.reshape(t.shape[0], -1)
def singleto... | byol-pytorch-master | byol_pytorch/byol_pytorch.py |
import os
import argparse
import multiprocessing
from pathlib import Path
from PIL import Image
import torch
from torchvision import models, transforms
from torch.utils.data import DataLoader, Dataset
from byol_pytorch import BYOL
import pytorch_lightning as pl
# test model, a resnet 50
resnet = models.resnet50(pre... | byol-pytorch-master | examples/lightning/train.py |
from setuptools import setup, find_packages
setup(
name = 'perceiver-pytorch',
packages = find_packages(),
version = '0.8.8',
license='MIT',
description = 'Perceiver - Pytorch',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https:... | perceiver-pytorch-main | setup.py |
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from perceiver_pytorch.perceiver_pytorch import exists, default, cache_fn, fourier_encode, PreNorm, FeedForward, Attention
# helpers
class Residual(nn.Module):
def __init__(self, fn):
super()._... | perceiver-pytorch-main | perceiver_pytorch/gated.py |
from math import pi, log
from functools import wraps
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def cache_fn(f):
cache = None
... | perceiver-pytorch-main | perceiver_pytorch/perceiver_io.py |
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from perceiver_pytorch.perceiver_pytorch import exists, default, cache_fn, fourier_encode, PreNorm, FeedForward, Attention
# linear attention
class LinearAttention(nn.Module):
def __init__(
sel... | perceiver-pytorch-main | perceiver_pytorch/experimental.py |
from perceiver_pytorch.perceiver_pytorch import Perceiver
from perceiver_pytorch.perceiver_io import PerceiverIO, PerceiverLM
| perceiver-pytorch-main | perceiver_pytorch/__init__.py |
from math import pi, log
from functools import wraps
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Reduce
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
... | perceiver-pytorch-main | perceiver_pytorch/perceiver_pytorch.py |
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from perceiver_pytorch.perceiver_pytorch import exists, default, cache_fn, fourier_encode, PreNorm, FeedForward, Attention
# latent mixer
def Mixer(seq_len, mult = 4, dropout = 0.):
return nn.Sequentia... | perceiver-pytorch-main | perceiver_pytorch/mixed_latents.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/deepbind_train_encode.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/deepbind_util.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/deepbind_train_rnac.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/deepfind.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/deepbind_train_selex.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/deepbind_train_dream5.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/report_plotter.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/hypertrain.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/util.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/globals.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/sgd.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/hpsearch.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/plug.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/tape2logo.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/trainer.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/node.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/gradcheck.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/report.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/data.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/dumpviz.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_lockfile/sqlitelockfile.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_lockfile/__init__.py |
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