repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
markveillette/high-fidelity-generative-compression | [
"d88b4d7f1212efa8611e91737ff6bf00bbf36670",
"d88b4d7f1212efa8611e91737ff6bf00bbf36670",
"d88b4d7f1212efa8611e91737ff6bf00bbf36670"
] | [
"src/loss/perceptual_similarity/dist_model.py",
"src/compression/entropy_models.py",
"src/loss/losses.py"
] | [
"\nfrom __future__ import absolute_import\n\nimport sys\nimport numpy as np\nimport torch\nfrom torch import nn\nimport os\nfrom collections import OrderedDict\nfrom torch.autograd import Variable\nimport itertools\nfrom .base_model import BaseModel\nfrom scipy.ndimage import zoom\nimport fractions\nimport functool... | [
[
"torch.optim.Adam",
"torch.mean",
"torch.clamp",
"torch.load",
"scipy.ndimage.zoom",
"numpy.cumsum",
"numpy.mean",
"numpy.argsort",
"torch.nn.DataParallel",
"numpy.array",
"numpy.sum",
"torch.autograd.Variable"
],
[
"torch.floor"
],
[
"torch.zeros_li... | [
{
"matplotlib": [],
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"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
SOPR-T/SOPR-T | [
"3242461fa8b3e917cde70be497beb1158a7b27e6",
"3242461fa8b3e917cde70be497beb1158a7b27e6"
] | [
"d3rlpy-master/tests/models/torch/test_dynamics.py",
"src/train_policy.py"
] | [
"import pytest\nimport torch\n\nfrom d3rlpy.models.encoders import DefaultEncoderFactory\nfrom d3rlpy.models.torch.dynamics import (\n ProbabilisticDynamicsModel,\n ProbabilisticEnsembleDynamicsModel,\n _compute_ensemble_variance,\n)\n\nfrom .model_test import DummyEncoder, check_parameter_updates\n\n\n@py... | [
[
"torch.allclose",
"torch.randint",
"torch.rand",
"torch.cat"
],
[
"torch.manual_seed",
"torch.cuda.is_available",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LockdownInnovators/CodeNames | [
"b82fc9c85d4887ae81f331de6f2058e5e2cdccd9"
] | [
"engine.py"
] | [
"from __future__ import print_function, division\n\nimport itertools\nimport re\nimport sys\nimport os\nimport platform\n\nimport numpy as np\n\nimport model\nfrom config import config\n\nCLUE_PATTERN = r'^([a-zA-Z]+) ({0})$'\nUNLIMITED = \"unlimited\"\n\n\n# noinspection PyAttributeOutsideInit\nclass GameEngine(ob... | [
[
"numpy.ones_like",
"numpy.random.RandomState",
"numpy.concatenate",
"numpy.array",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aaxwaz/youtube-8m | [
"3c3ceae83173d6b9eaef6072308a2804ba56bcf5",
"3c3ceae83173d6b9eaef6072308a2804ba56bcf5"
] | [
"other_train/train_loadCorrMat.py",
"other_frame_level_model/FV_fv1Only_SVDMidTanh_hiddenLayer/frame_level_models.py"
] | [
"# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by app... | [
[
"tensorflow.device",
"tensorflow.concat",
"tensorflow.gfile.DeleteRecursively",
"tensorflow.python.client.device_lib.list_local_devices",
"tensorflow.control_dependencies",
"tensorflow.flags.FlagsError",
"tensorflow.stack",
"tensorflow.gfile.Exists",
"tensorflow.cast",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.4",
"1.5",
"1.7",
"1.0",
"1.2"
]
}
] |
sergimasot/PycQED_py3 | [
"54ad1b14929ffe5cc87cf59423a970e4b9baa3e1",
"54ad1b14929ffe5cc87cf59423a970e4b9baa3e1",
"54ad1b14929ffe5cc87cf59423a970e4b9baa3e1"
] | [
"pycqed/measurement/waveform_control/pulsar.py",
"pycqed/measurement/pulse_sequences/multi_qubit_tek_seq_elts.py",
"pycqed/analysis/tools/cryoscope_tools.py"
] | [
"# Originally by Wolfgang Pfaff\n# Modified by Adriaan Rol 9/2015\n# Modified by Ants Remm 5/2017\n# Modified by Michael Kerschbaum 5/2019\nimport os\nimport shutil\nimport ctypes\nimport numpy as np\nimport logging\nfrom qcodes.instrument.base import Instrument\nfrom qcodes.instrument.parameter import (\n Manua... | [
[
"numpy.savetxt",
"numpy.array",
"numpy.sum"
],
[
"numpy.imag",
"numpy.reshape",
"numpy.arange",
"numpy.eye",
"numpy.sin",
"numpy.ndim",
"numpy.ones",
"numpy.real",
"numpy.any",
"numpy.ravel",
"numpy.array",
"numpy.zeros",
"numpy.sum"
],
[
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
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"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"... |
PeterXingke/HugeCTR | [
"d7552c4c5f93ff18ded961645cac82d5d8b5b785"
] | [
"sparse_operation_kit/unit_test/test_scripts/tf2/test_sparse_emb_demo_model_multi_worker.py"
] | [
"\"\"\"\n Copyright (c) 2021, NVIDIA CORPORATION.\n \n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable l... | [
[
"tensorflow.nn.compute_average_loss",
"tensorflow.concat",
"tensorflow.shape",
"tensorflow.keras.losses.BinaryCrossentropy",
"numpy.concatenate",
"tensorflow.distribute.MultiWorkerMirroredStrategy",
"tensorflow.GradientTape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
avpak/okama | [
"b3c4f6b7dfcc314d3171f20b3bc95cfa04268c1a"
] | [
"tests/test_frontier.py"
] | [
"import pytest\nfrom pytest import approx\nfrom pytest import mark\n\nimport numpy as np\nfrom numpy.testing import assert_allclose\n\nfrom okama import EfficientFrontier\n\n\n@mark.frontier\ndef test_init_efficient_frontier():\n with pytest.raises(Exception, match=r'The number of symbols cannot be less than two... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KKanda900/Model-Maker | [
"e73c6e1d47b9682657694e4f56ee96a34e3a29ea"
] | [
"Multi_Classification/Multi_Image_Classification.py"
] | [
"# Primary Python Files for Image Classification\nimport numpy as np \nimport pandas as pd \nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # dont show any tensorflow warning messages\nimport cv2\n\n# Keras libraries used for making the model and tensorflow\nimport tensorflow, keras\nfrom tensorflow.keras.util... | [
[
"numpy.random.seed",
"sklearn.model_selection.train_test_split",
"numpy.argmax",
"numpy.array",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
BeeQC/ANODE-reproducibility | [
"9d6b5a297302cdaa0bbc3908de1a94f3c28c0606"
] | [
"experiments/experiments_img.py"
] | [
"import json\nimport matplotlib\nmatplotlib.use('Agg') # This is hacky (useful for running on VMs)\nimport numpy as np\nimport os\nimport time\nimport torch\nfrom anode.models import ODENet\nfrom anode.conv_models import ConvODENet\nfrom anode.discrete_models import ResNet\nfrom anode.training import Trainer\nfrom... | [
[
"matplotlib.use",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
physwkim/silx | [
"e3f39babad34c97db8ec5dfbb8e92287ce059f70",
"3f9bcda88c074438fdb30cde29fec314d26f471c",
"3f9bcda88c074438fdb30cde29fec314d26f471c",
"3f9bcda88c074438fdb30cde29fec314d26f471c",
"e3f39babad34c97db8ec5dfbb8e92287ce059f70",
"3f9bcda88c074438fdb30cde29fec314d26f471c",
"e3f39babad34c97db8ec5dfbb8e92287ce059f7... | [
"silx/gui/plot/actions/io.py",
"silx/io/test/test_specfile.py",
"silx/gui/data/_VolumeWindow.py",
"silx/math/fft/fftw.py",
"silx/gui/fit/FitWidget.py",
"silx/math/test/test_HistogramndLut_nominal.py",
"silx/image/_boundingbox.py",
"silx/gui/widgets/LegendIconWidget.py"
] | [
"# coding: utf-8\n# /*##########################################################################\n#\n# Copyright (c) 2004-2020 European Synchrotron Radiation Facility\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Soft... | [
[
"numpy.arange",
"numpy.save",
"numpy.ones",
"numpy.zeros_like",
"numpy.isscalar"
],
[
"numpy.sum"
],
[
"numpy.isfinite",
"numpy.std",
"numpy.mean",
"numpy.any",
"numpy.iscomplexobj"
],
[
"numpy.copy"
],
[
"numpy.arange"
],
[
"numpy.array_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
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"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Nijta/project-NN-Pytorch-scripts | [
"06a50ab072613fb60b8b8e1cea85c4aa8e75549d",
"06a50ab072613fb60b8b8e1cea85c4aa8e75549d"
] | [
"project/03-asvspoof-mega/03_fuse_score_evaluate.py",
"sandbox/dynamic_prog.py"
] | [
"#!/usr/bin/python\n\"\"\" \nWrapper to fuse score and compute EER and min tDCF\nSimple score averaging.\n\nUsage:\npython 03_fuse_score_evaluate.py log_output_testset_1 log_output_testset_2 ...\n\nThe log_output_testset is produced by the pytorch code, for\nexample, ./lfcc-lcnn-lstmsum-am/01/__pretrained/log_outpu... | [
[
"numpy.array",
"numpy.mean"
],
[
"torch.zeros",
"numpy.arange",
"torch.zeros_like",
"torch.arange",
"torch.finfo",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
onsabbatical/PoET-BiN | [
"5226cf7e8e34316a3ced73ce30528ac49730ecf4",
"5226cf7e8e34316a3ced73ce30528ac49730ecf4",
"5226cf7e8e34316a3ced73ce30528ac49730ecf4",
"5226cf7e8e34316a3ced73ce30528ac49730ecf4"
] | [
"mnist/storage.py",
"mnist/main.py",
"svhn/rinc/lvl_wise_copy2.py",
"mnist/classifier/my_pool_multi_1.py"
] | [
"import torch \nimport numpy as np\n\ndef store_value(main_array,cu_fl,i,name):\n\n\tcu_uint8 = cu_fl.type(torch.ByteTensor)\n\tmain_array = torch.cat((main_array,cu_uint8),0)\n\t#print(i)\n\n\tif (i + 1)%100 == 0:\n\t\tmain_array_np = main_array.cpu().numpy()\n\t\tnp.save(name + str(int(i/100)) + '.npy',main_array... | [
[
"torch.ByteTensor",
"numpy.shape",
"torch.Tensor",
"torch.cat"
],
[
"torch.ByteTensor",
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.DataParallel",
"torch.save"
],
[
"nump... | [
{
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"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
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"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
keadwen/CFU-Playground | [
"74c79158e85e1365170ececd1d91ea3fa48faba0"
] | [
"third_party/tflite-micro/tensorflow/lite/micro/tools/metrics/create_size_log.py"
] | [
"# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
chandar-lab/IIRC | [
"ae6ffcfc0a42274bcda66e2288e09118604620e4"
] | [
"experiments/utils.py"
] | [
"import numpy as np\nimport torch.nn as nn\nimport json\n\n\ndef log(epoch, task_id, log_dict, logbook):\n log_dict[\"message\"] = f\"task_{task_id}_metrics\"\n log_dict[\"task_id\"] = task_id\n log_dict[\"task_epoch\"] = epoch\n log_dict[\"step\"] = epoch\n logbook.write_metric(log_dict)\n\n\ndef lo... | [
[
"numpy.random.random",
"torch.nn.functional.pad",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carlo-/RNNet | [
"995fcce1da58ac2c840afd865bde88d11d81006f"
] | [
"experiments.py"
] | [
"#\n# KTH Royal Institute of Technology\n# DD2424: Deep Learning in Data Science\n# Assignment 4\n#\n# Carlo Rapisarda (carlora@kth.se)\n#\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport dataset as dt\nfrom os.path import exists\nfrom model import RNNet\nfrom utilities import compute_grads_numerical,... | [
[
"numpy.random.seed",
"numpy.arange",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mpekalski/Y8M | [
"24b61107a0f482fdb36ab8b15b768cea24e5808a",
"24b61107a0f482fdb36ab8b15b768cea24e5808a"
] | [
"video_level_code/xp_frame_level_models.py",
"bstnet/readers.py"
] | [
"# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by app... | [
[
"tensorflow.nn.dynamic_rnn",
"tensorflow.concat",
"tensorflow.contrib.slim.l2_regularizer",
"tensorflow.nn.moments",
"tensorflow.nn.top_k",
"tensorflow.name_scope",
"tensorflow.random_normal_initializer",
"tensorflow.matmul",
"tensorflow.contrib.slim.batch_norm",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
MaZhanyu007/MSDGAN | [
"037ad33025c29869dbc9cb233a45b8762d31179d"
] | [
"decoder.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n# In[2]:\n\n\nclass Decoder(nn.Module):\n def __init__(self, output_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout_rate, attention):\n super().__init__()\n \n s... | [
[
"torch.nn.Dropout",
"torch.cat",
"torch.nn.GRU",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.bmm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
opesci/seigen | [
"7d12eab05ed5a857601babe2933aa804c853de66"
] | [
"tests/tiling/explosive_source.py"
] | [
"\"\"\"\nThis is an explicit DG method: we invert the mass matrix and perform\na matrix-vector multiplication to get the solution in a time step\n\"\"\"\n\nfrom math import *\nimport mpi4py\nimport numpy as np\nfrom time import time\nimport sys\nimport os\nimport cProfile\n\nfrom firedrake import *\nfrom firedrake.... | [
[
"numpy.arange",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isabella232/gps_building_blocks | [
"86ef8be60a42cd12e27696007589388b7b053f4f",
"86ef8be60a42cd12e27696007589388b7b053f4f"
] | [
"py/gps_building_blocks/analysis/exp_design/ab_testing_design_test.py",
"py/gps_building_blocks/ml/data_prep/data_visualizer/viz_utils_test.py"
] | [
"# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.array"
],
[
"pandas.testing.assert_frame_equal",
"matplotlib.pyplot.subplots",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"... |
ashishpatel26/mealpy | [
"69e8dc727e15527e31ac5ace1debe92a0bc7d828",
"69e8dc727e15527e31ac5ace1debe92a0bc7d828",
"69e8dc727e15527e31ac5ace1debe92a0bc7d828",
"69e8dc727e15527e31ac5ace1debe92a0bc7d828"
] | [
"mealpy/fake/RHO.py",
"mealpy/swarm_based/BES.py",
"mealpy/bio_based/IWO.py",
"mealpy/physics_based/WDO.py"
] | [
"#!/usr/bin/env python\n# ------------------------------------------------------------------------------------------------------%\n# Created by \"Thieu Nguyen\" at 14:53, 17/03/2020 %\n# ... | [
[
"numpy.dot",
"numpy.linalg.norm",
"numpy.ones",
"numpy.random.normal",
"numpy.mean",
"numpy.exp",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
],
[
"numpy.cosh",
"numpy.cos",
"numpy.sinh",
"numpy.sin",
"numpy.max",
"numpy.mean",
"numpy.ra... | [
{
"matplotlib": [],
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"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
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"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
intel-isl/MetaLearningTradeoffs | [
"bb1b849742a959310f3b9b630bb76ae3509a5d4a",
"bb1b849742a959310f3b9b630bb76ae3509a5d4a",
"bb1b849742a959310f3b9b630bb76ae3509a5d4a"
] | [
"maml_zoo/baselines/zero_baseline.py",
"experiments/benchmark/summary.py",
"maml_zoo/meta_trainer.py"
] | [
"from maml_zoo.baselines.base import Baseline\nimport numpy as np\n\n\nclass ZeroBaseline(Baseline):\n \"\"\"\n Dummy baseline\n \"\"\"\n\n def __init__(self):\n super(ZeroBaseline, self).__init__()\n\n def get_param_values(self, **kwargs):\n \"\"\"\n Returns the parameter values... | [
[
"numpy.zeros_like"
],
[
"matplotlib.pyplot.tight_layout",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"matplotlib.use",
"matplotlib.pyplot.ylabel",
"scipy.special.betainc",
"numpy.mean",
"matplotlib.pyplot.close",
"numpy.var",
"matplotlib.pyplot.xlabel",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"... |
ouyangyike/Inference-Algorithm | [
"ac3470e2fbc4415174b32ecc2e2f3f101da1ca38",
"ac3470e2fbc4415174b32ecc2e2f3f101da1ca38"
] | [
"logistic regression/logistic_adam/adam_train_loss .py",
"logistic regression/softmax/soft_test_accuracy .py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom logistic_adam import *\n\n\n#learing rate = 1,batch_size = 500, epoch=15, lamda = 0.01\nlogging = runLogistic(1,500,15,0.01)\n#print(logging)\nplt.plot(logging[:,0],marker='+',label='learning rate = 1')\n\n#learing rate = 0.1,batch_... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NinaTian98369/HypoGen | [
"14f192ecc1ef0c6fc5864f0816ef61885dc9e864"
] | [
"Code/HypoBertClas/pybert/test/predicter.py"
] | [
"#encoding:utf-8\nimport torch\nimport numpy as np\nfrom ..utils.utils import model_device,load_bert\n\nclass Predicter(object):\n def __init__(self,\n model,\n logger,\n n_gpu,\n model_path\n ):\n self.model = model\n ... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StarWang/detext | [
"66f071ec2cebf5e54e7d1de40936b5f281c2a69b",
"66f071ec2cebf5e54e7d1de40936b5f281c2a69b"
] | [
"src/smart_compose/train/data_fn.py",
"test/detext/layers/test_vocab_layer.py"
] | [
"import tensorflow as tf\nfrom functools import partial\n\nfrom smart_compose.utils.parsing_utils import get_input_files, InputFtrType, iterate_items_with_list_val\n\n\ndef _read_specified_features(inputs, feature_type2name):\n \"\"\"Only reads in features specified in the DeText arguments\"\"\"\n required_in... | [
[
"tensorflow.constant",
"tensorflow.data.TFRecordDataset",
"tensorflow.io.parse_single_example",
"tensorflow.cast",
"tensorflow.io.FixedLenFeature"
],
[
"tensorflow.saved_model.save",
"tensorflow.constant",
"tensorflow.test.main",
"tensorflow.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1... |
wensun/baselines | [
"81b7b988918de2c1c2f5fa9f38b7716608efc125"
] | [
"baselines/ddpg/main.py"
] | [
"import argparse\nimport time\nimport os\nimport logging\nfrom baselines import logger, bench\nfrom baselines.common.misc_util import (\n set_global_seeds,\n boolean_flag,\n)\n#import baselines.ddpg.training as training\nimport training as training\nfrom baselines.ddpg.models import Actor, Critic\nfrom baseli... | [
[
"tensorflow.reset_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
Ziaeemehr/brian2 | [
"0d28f61881a033f877fb333b5e93c56e5c479b4b"
] | [
"brian2/tests/test_codegen.py"
] | [
"\nfrom collections import namedtuple\nimport os\n\nimport numpy as np\nimport pytest\n\nfrom brian2 import prefs, clear_cache, _cache_dirs_and_extensions\nfrom brian2.codegen.cpp_prefs import compiler_supports_c99\nfrom brian2.codegen.optimisation import optimise_statements\nfrom brian2.codegen.translation import ... | [
[
"numpy.issubdtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kronemeyer/project-athena | [
"0e79cba1c4d30146326ce7bd311f69f2ee845e80"
] | [
"src/attacks/attack.py"
] | [
"\"\"\"\nImplement white-box attacks on top of IBM ART.\n@author: Ying Meng (y(dot)meng201011(at)gmail(dot)com)\n\"\"\"\n\nimport numpy as np\nimport torch\n\n# from art.attacks.evasion.fast_gradient import FastGradientMethod\n# from art.attacks.evasion.projected_gradient_descent import ProjectedGradientDescent\nfr... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
visr/neuralhydrology | [
"77f6c9214945c8e857e3b9545afe8470da751cab",
"77f6c9214945c8e857e3b9545afe8470da751cab"
] | [
"neuralhydrology/datasetzoo/camelsus.py",
"neuralhydrology/modelzoo/ealstm.py"
] | [
"from pathlib import Path\nfrom typing import Dict, List, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\nimport xarray\n\nfrom neuralhydrology.datasetzoo.basedataset import BaseDataset\nfrom neuralhydrology.utils.config import Config\n\n\nclass CamelsUS(BaseDataset):\n \"\"\"Data set class for the CAME... | [
[
"pandas.concat",
"pandas.read_csv"
],
[
"torch.nn.Dropout",
"torch.sigmoid",
"torch.cat",
"torch.nn.init.constant_",
"torch.eye",
"torch.tanh",
"torch.nn.Linear",
"torch.FloatTensor",
"torch.nn.init.orthogonal_",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
biomac-lab/covid19_forecast | [
"6613064f8a6d8023ecbdaddbc2e7525b6ad0796f"
] | [
"functions/plot_utils.py"
] | [
"from matplotlib.dates import date2num, num2date\nfrom matplotlib.colors import ListedColormap\nfrom matplotlib import dates as mdates\nfrom matplotlib.patches import Patch\nfrom matplotlib import pyplot as plt\nfrom matplotlib import ticker\n\nimport os\n\ndef plot_fit(df_fit, df_data, y_label='Deaths', y_lim_up =... | [
[
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.tight_layout",
"matplotlib.dates.WeekdayLocator",
"matplotlib.ticker.StrMethodFormatter",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.close",
"matplotlib.dates.DayLocator",
"matplotlib.dates.MonthLocator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rekhabiswal/sage | [
"e8633b09919542a65e7e990c8369fee30c7edefd"
] | [
"src/sage/plot/arrow.py"
] | [
"\"\"\"\nArrows\n\"\"\"\n#*****************************************************************************\n# Copyright (C) 2006 Alex Clemesha <clemesha@gmail.com>,\n# William Stein <wstein@gmail.com>,\n# 2008 Mike Hansen <mhansen@gmail.com>,\n# 20... | [
[
"matplotlib.path.Path",
"numpy.array",
"numpy.array_equal",
"matplotlib.patheffects.Stroke"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nirmal1313/Regression-Methods | [
"b1f885dc798ca4aae47661e0a27fe0e21e4ee4e0"
] | [
"Linear_Ridge_Regression .py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd # for working with data in Python\nimport numpy as np\nimport matplotlib.pyplot as plt # for visualization\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn import linear_mod... | [
[
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"numpy.exp",
"pandas.read_csv",
"matplotlib.pyplot.style.use",
"numpy.log",
"matplotlib.pyplot.title",
"matplotlib.pyplot.annotate",
"sklearn.model_selection.train_test_split",
"sklearn.linear_model.Ridge",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Nintorac/survae_experiments | [
"d68cc25e2604aab08b53617c1f3ffe4716f166c4"
] | [
"survae/transforms/bijections/conv1x1.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom survae.transforms.bijections import Bijection\n\n\nclass Conv1x1(Bijection):\n \"\"\"\n Invertible 1x1 Convolution [1].\n The weight matrix is initialized as a random rotation matrix\n as described in Section... | [
[
"torch.nn.init.uniform_",
"numpy.sqrt",
"torch.Tensor",
"torch.slogdet",
"torch.inverse",
"torch.nn.init.orthogonal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WarrenWeckesser/numtypes | [
"4e46ac4a338ab46eec11cbacf9165827841ea4ff"
] | [
"numtypes/tests/test_nint32.py"
] | [
"\nimport pytest\nimport math\nimport numpy as np\nfrom numpy.testing import assert_equal\nfrom numtypes import nint32\n\n\ndef test_basic():\n x = nint32(3)\n assert x == 3\n assert int(x) == 3\n\n\n@pytest.mark.parametrize('typ', [np.int8, np.uint8, np.int16, np.uint16,\n ... | [
[
"numpy.isnan",
"numpy.testing.assert_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PApostol/pandas | [
"578e918777f6f512f85a917dc34910df87f63e90"
] | [
"pandas/tests/util/test_show_versions.py"
] | [
"import json\nimport os\nimport re\n\nimport pytest\n\nfrom pandas.compat import (\n IS64,\n is_ci_environment,\n)\nfrom pandas.util._print_versions import (\n _get_dependency_info,\n _get_sys_info,\n)\n\nimport pandas as pd\n\n\n@pytest.mark.filterwarnings(\n # openpyxl\n \"ignore:defusedxml.lxml... | [
[
"pandas.compat.is_ci_environment",
"pandas.util._print_versions._get_dependency_info",
"pandas.util._print_versions._get_sys_info",
"pandas.show_versions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
riokt/video-paragraph | [
"2da3298819e73809af495457db2cf1dfffad712f",
"2da3298819e73809af495457db2cf1dfffad712f"
] | [
"metrics/evaluation.py",
"modules/transformer.py"
] | [
"from cap_eval.bleu.bleu import Bleu\nfrom cap_eval.cider.cider import Cider\nfrom cap_eval.meteor.meteor import Meteor\n\nimport json\nimport numpy as np\n\n# initialize the caption evaluators\nmeteor_scorer = Meteor()\ncider_scorer = Cider()\nbleu_scorer = Bleu(4)\n\n\ndef bleu_eval(refs, cands):\n print (\"calc... | [
[
"numpy.mean"
],
[
"torch.nn.Dropout",
"torch.max",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.from_numpy",
"torch.multinomial",
"numpy.ones",
"torch.exp",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Noahs-ARK/PaLM | [
"fe943bb0516d80b09f2b56de60dac9c54dc196e6"
] | [
"eval.py"
] | [
"import math\nimport numpy as np\nimport torch\nimport data\nfrom torch.autograd import Variable\nfrom utils import batchify, get_batch, repackage_hidden\nimport argparser\nargs = argparser.args()\nfrom utils import Input\n\n# Set the random seed manually for reproducibility.\nnp.random.seed(args.seed)\ntorch.manua... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.random.seed",
"torch.load",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.nn.functional.log_softmax",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.functional.linear",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
javiergodoy/pandas-profiling | [
"0bed133520b9982263ed8cbc1af6b8f5a511bf0d",
"0bed133520b9982263ed8cbc1af6b8f5a511bf0d",
"0bed133520b9982263ed8cbc1af6b8f5a511bf0d"
] | [
"tests/unit/test_url.py",
"tests/issues/test_issue51.py",
"examples/website_inaccessibility/website_inaccessibility.py"
] | [
"import pandas as pd\nimport numpy as np\n\nimport pandas_profiling\n\n\ndef test_urls(get_data_file):\n file_name = get_data_file(\n \"whitelist_urls.csv\",\n \"https://raw.githubusercontent.com/openeventdata/scraper/master/whitelist_urls.csv\",\n )\n\n df = pd.read_csv(\n file_name, ... | [
[
"pandas.read_csv",
"numpy.random.random"
],
[
"pandas.DataFrame"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
... |
Lguiller/machinelearning-az | [
"7c062302944b91131783fe663e1cff21e5956ca2"
] | [
"datasets/Part 2 - Regression/Section 6 - Polynomial Regression/polinomial_regression.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 5 12:45:44 2019\n\n@author: juangabriel\n\"\"\"\n\n# Regresión polinómica\n\n# Cómo importar las librerías\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importar el data set\ndataset = pd.read_csv('Positio... | [
[
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
sighingnow/mars | [
"c7897fbd144d230fff5edabc1494fb3ff44aa0d2",
"c7897fbd144d230fff5edabc1494fb3ff44aa0d2",
"c7897fbd144d230fff5edabc1494fb3ff44aa0d2",
"c7897fbd144d230fff5edabc1494fb3ff44aa0d2",
"c7897fbd144d230fff5edabc1494fb3ff44aa0d2",
"c7897fbd144d230fff5edabc1494fb3ff44aa0d2",
"c7897fbd144d230fff5edabc1494fb3ff44aa0d... | [
"mars/tensor/reduction/nanargmin.py",
"mars/worker/tests/test_calc.py",
"mars/worker/storage/tests/test_procmem_io.py",
"mars/tensor/random/vonmises.py",
"mars/tensor/indexing/unravel_index.py",
"mars/tensor/tests/test_core_execute.py",
"mars/worker/tests/test_transfer.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2018 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/li... | [
[
"numpy.dtype"
],
[
"numpy.random.random"
],
[
"numpy.random.random"
],
[
"numpy.random.RandomState",
"numpy.dtype"
],
[
"numpy.dtype"
],
[
"numpy.dot",
"numpy.swapaxes",
"numpy.random.random",
"numpy.squeeze",
"numpy.ones",
"numpy.testing.assert_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
nabobalis/glue | [
"1c718378b5527e64d85cc6a6f9a0330652e5cf4b"
] | [
"glue/viewers/image/composite_array.py"
] | [
"# This artist can be used to deal with the sampling of the data as well as any\n# RGB blending.\n\nimport numpy as np\n\nfrom matplotlib.colors import ColorConverter, Colormap\nfrom astropy.visualization import (LinearStretch, SqrtStretch, AsinhStretch,\n LogStretch, ManualInterva... | [
[
"numpy.product",
"numpy.clip",
"numpy.isnan",
"numpy.dtype",
"numpy.ones",
"numpy.atleast_2d",
"matplotlib.colors.ColorConverter",
"numpy.isscalar",
"numpy.any",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SalAlba/matplotlib | [
"f73ff4e77074152fb9abc400d66f56111e656687"
] | [
"tutorial/basic/ex3.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nfrom sal_timer import timer\n\n\n\ndef plot_1():\n # ...\n data = {\n 'a': np.arange(50),\n 'c': np.random.randint(0, 50, 50),\n 'd': np.random.randn(50)\n }\n data['b'] = data['a'] + 10 * np.random.randn(50)\n data['d'] =... | [
[
"numpy.abs",
"matplotlib.pyplot.scatter",
"numpy.arange",
"numpy.random.randint",
"numpy.random.randn",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eherr/vis_utils | [
"b757b01f42e6da02ad62130c3b0e61e9eaa3886f",
"b757b01f42e6da02ad62130c3b0e61e9eaa3886f",
"b757b01f42e6da02ad62130c3b0e61e9eaa3886f"
] | [
"vis_utils/graphics/geometry/splines.py",
"vis_utils/animation/point_cloud_animation_controller.py",
"vis_utils/graphics/light/directional_light.py"
] | [
"#!/usr/bin/env python\n#\n# Copyright 2019 DFKI GmbH.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limitation the rights to use, copy... | [
[
"numpy.dot",
"scipy.interpolate.splrep",
"numpy.linspace",
"numpy.arange",
"numpy.linalg.norm",
"scipy.interpolate.splev",
"numpy.array",
"numpy.zeros",
"numpy.sum"
],
[
"numpy.dot",
"numpy.array"
],
[
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
truthiswill/federated | [
"d25eeac036dfc2a485120a195fd904223cfc823a",
"d25eeac036dfc2a485120a195fd904223cfc823a"
] | [
"tensorflow_federated/python/aggregators/quantile_estimation_test.py",
"tensorflow_federated/examples/stateful_clients/stateful_fedavg_tff.py"
] | [
"# Copyright 2020, The TensorFlow Federated Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by a... | [
[
"numpy.exp"
],
[
"tensorflow.zeros_like",
"tensorflow.keras.optimizers.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
s3a-spatialaudio/VISR | [
"55f6289bc5058d4898106f3520e1a60644ffb3ab",
"55f6289bc5058d4898106f3520e1a60644ffb3ab",
"55f6289bc5058d4898106f3520e1a60644ffb3ab"
] | [
"src/python/scripts/rsao/reverbObjectBinauralisation_flexible.py",
"src/python/packages/visr_bst/renderers/hoa_binaural_renderer.py",
"src/python/packages/visr_bst/hoa_components/hoa_object_encoder.py"
] | [
" # -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 14 15:59:11 2017\n\n@author: af5u13\n\"\"\"\n\n# Usage for debugging from raw Python console\n#exec(open(\"/Users/af5u13/dev/visr/src/python/scripts/rsao/reverbObjectBinauralisation.py\").read())\n\nimport visr\nimport signalflows\nimport panning\nimport pml... | [
[
"numpy.concatenate",
"numpy.arange",
"numpy.array"
],
[
"numpy.concatenate",
"numpy.sqrt"
],
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Woooosz/dgl | [
"729ff2ef385f302af562c8305b1006d067d2067f",
"729ff2ef385f302af562c8305b1006d067d2067f"
] | [
"examples/pytorch/gcmc/model.py",
"python/dgl/nn/pytorch/conv/tagconv.py"
] | [
"\"\"\"NN modules\"\"\"\nimport torch as th\nimport torch.nn as nn\nfrom torch.nn import init\nimport dgl.function as fn\nimport dgl.nn.pytorch as dglnn\n\nfrom utils import get_activation\n\nclass GCMCGraphConv(nn.Module):\n \"\"\"Graph convolution module used in the GCMC model.\n\n Parameters\n ---------... | [
[
"torch.nn.Dropout",
"torch.Tensor",
"torch.cat",
"torch.nn.ParameterDict",
"torch.einsum",
"torch.randn",
"torch.nn.Linear",
"torch.nn.ParameterList",
"torch.nn.init.xavier_uniform_"
],
[
"torch.nn.init.calculate_gain",
"torch.cat",
"torch.reshape",
"torch.n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xyt556/rsnet | [
"5f20f5308f89695e9f26ee4724d5591201d0c52d"
] | [
"rsnet/dataset/raster.py"
] | [
"import os\n\nimport rasterio\nimport numpy as np\n\nfrom ..utils import pair, bytescale\nfrom .base import BaseRasterData\n\n\nclass RasterSampleDataset(BaseRasterData):\n \"\"\"Dataset wrapper for remote sensing data.\n\n Args:\n fname:\n win_size:\n step_size:\n pad_size:\n ... | [
[
"numpy.dtype",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KainRasleafar/sedfitter | [
"4f0e9e46f7903a853166835bb74857cc15eef219",
"4f0e9e46f7903a853166835bb74857cc15eef219",
"4f0e9e46f7903a853166835bb74857cc15eef219"
] | [
"sedfitter/sed/sed.py",
"sedfitter/fitting_routines.py",
"sedfitter/filter/filter.py"
] | [
"from __future__ import print_function, division\n\nimport os\n\nimport numpy as np\nfrom astropy import log\nfrom astropy.io import fits\nfrom astropy.table import Table\nfrom scipy.interpolate import interp1d\nfrom astropy import units as u\n\nfrom ..utils.validator import validate_array\n\nfrom .helpers import p... | [
[
"numpy.argsort",
"numpy.log10",
"numpy.array"
],
[
"numpy.isinf",
"numpy.log",
"numpy.where",
"numpy.sum"
],
[
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rick0514/VPR_SMCN | [
"7a00dc8e4de0c21438474c05a4a7be18d05367fa"
] | [
"main/MCN.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport main.utils as utils\nimport time\n\n# ---------------------------- 说明 ----------------------------------\n# MCN的python复现\n# ---------------------------- 说明 ----------------------------------\n\n\nclass MCNParams:\n \"\"\"\n a struct define the input... | [
[
"numpy.sum",
"matplotlib.pyplot.title",
"numpy.empty_like",
"numpy.arange",
"matplotlib.pyplot.ylim",
"numpy.linalg.norm",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.zeros_like",
"numpy.random.rand",
"numpy.random.randint",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FynnBe/pytorch-3dunet | [
"34918e82c3afeff02360b03964de973eac3a4f75"
] | [
"pytorch3dunet/augment/transforms.py"
] | [
"import importlib\n\nimport numpy as np\nimport torch\nfrom scipy.ndimage import rotate, map_coordinates, gaussian_filter\nfrom scipy.ndimage.filters import convolve\nfrom skimage.filters import gaussian\nfrom skimage.segmentation import find_boundaries\nfrom torchvision.transforms import Compose\n\n# WARN: use fix... | [
[
"numpy.rot90",
"numpy.expand_dims",
"numpy.pad",
"numpy.clip",
"numpy.unique",
"numpy.arange",
"numpy.stack",
"scipy.ndimage.rotate",
"numpy.concatenate",
"numpy.logical_or.reduce",
"numpy.zeros_like",
"scipy.ndimage.map_coordinates",
"numpy.flip",
"numpy.tr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": [... |
krevas/ET-BERT | [
"464ce3e7942d4450f55021e267ceb9dd48a36b1f"
] | [
"uer/layers/layer_norm.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass LayerNorm(nn.Module):\n \"\"\"\n Layer Normalization.\n https://arxiv.org/abs/1607.06450\n \"\"\"\n def __init__(self, hidden_size, eps=1e-6):\n super(LayerNorm, self).__init__()\n self.eps = eps\n self.gamma = nn.Parameter(torch.on... | [
[
"torch.rsqrt",
"torch.ones",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xupsh/pp4fpgas-cn-hls | [
"d14bd0769ce7f9674f206faf93b7622c5bf905bf"
] | [
"hw/ip/mono_fm/transform.py"
] | [
"import numpy as np\ndetection_file = 'samples.npy'\ndetections = None\nif detection_file is not None:\n detections = np.load(detection_file)\nnp.savetxt('samples.txt', detections, fmt='%0.18f')\n\nf = open('samples.txt')\nout = open('complex.txt', \"w\")\nlines = f.readlines()\nfor line in lines:\n for i in ... | [
[
"numpy.savetxt",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Emieeel/OpenFermion | [
"865d8591cad9b0681f6dd25a391a5292ed2de1d4",
"865d8591cad9b0681f6dd25a391a5292ed2de1d4",
"c19d9667c5970473893f9bc0183556c4cd354dd7",
"865d8591cad9b0681f6dd25a391a5292ed2de1d4",
"865d8591cad9b0681f6dd25a391a5292ed2de1d4"
] | [
"src/openfermion/utils/rdm_mapping_functions_test.py",
"src/openfermion/circuits/trotter_exp_to_qgates.py",
"src/openfermion/measurements/vpe_estimators_test.py",
"src/openfermion/testing/testing_utils.py",
"src/openfermion/ops/operators/binary_code.py"
] | [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software... | [
[
"numpy.eye",
"numpy.allclose"
],
[
"numpy.arange",
"numpy.real",
"numpy.vstack"
],
[
"numpy.dot",
"pandas.Series",
"numpy.exp",
"numpy.array",
"numpy.sum",
"numpy.random.RandomState",
"numpy.isclose"
],
[
"scipy.linalg.qr",
"numpy.random.seed",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4... |
NTR0314/botorch | [
"f0310c9a415947f3264dac7f3438744784843323",
"f0310c9a415947f3264dac7f3438744784843323",
"f0310c9a415947f3264dac7f3438744784843323"
] | [
"botorch/test_functions/multi_objective.py",
"test/acquisition/test_utils.py",
"test/models/test_converter.py"
] | [
"#! /usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nr\"\"\"\nMulti-objective optimization benchmark problems.\n\nReferences\n\n.. [Deb2005dtlz]\n K. Deb, L.... | [
[
"torch.linspace",
"torch.Size",
"scipy.special.gamma",
"torch.cat",
"torch.sin",
"torch.min",
"torch.eye",
"torch.arange",
"torch.tensor",
"torch.exp",
"torch.split",
"torch.stack",
"torch.cos"
],
[
"torch.all",
"torch.Size",
"torch.ones",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mmoussallam/bird | [
"6a362de7d3a52dfcddaed13e8c736d039b03fbb4"
] | [
"bird/tests/test_mdct_tools.py"
] | [
"# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Manuel Moussallam <manuel.moussallam@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal\nfrom bird.mdct_tools import mdct, imdct\n\n\ndef test_mdct():\n \"Test mdct and imdct ... | [
[
"numpy.arange",
"numpy.random.RandomState",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anicokatz/PyMultiNestPlus | [
"d223ac90bef7c1b61e337b70c2bdb41ed46cb2b7"
] | [
"example_workspace/inverted_hierarchy/model.py"
] | [
"# INVERTED HIERARCHY\nimport prior_handler as phandle\nimport math\nimport numpy as np\nimport os\ncwd = os.path.dirname(os.path.realpath(__file__))\nprint(cwd)\n\nprior_handler = phandle.PriorHandler(cwd)\ncon = prior_handler.c\nn_pars = prior_handler.n_pars\n\ndef prior(cube, n_dims, n_pars):\n return prior_h... | [
[
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elke0011/OpenFlightSim | [
"1e28c54864ffd188f27425c8a71cce8b70a4bd7f"
] | [
"Utilities/JSBSimWriteXml.py"
] | [
"\"\"\"\nUniversity of Minnesota\nAerospace Engineering and Mechanics - UAV Lab\nCopyright 2019 Regents of the University of Minnesota.\nSee: LICENSE.md for complete license details.\n\nAuthor: Louis Mueller, Chris Regan\n\"\"\"\n\nimport os.path\nfrom xml.etree import ElementTree as ET\n\nimport numpy as np\n\n\nf... | [
[
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CBICA/MUSE | [
"edd01964078f957101130993899c7f4de13d48b6"
] | [
"src/muse-combineRoiMapsIter.py"
] | [
"#!/usr/bin/env python\n#\n# @file muse_combineRoiMapsIter.py\n# @brief Combine roi probability maps for a single subject\n#\n# Copyright (c) 2011, 2012 University of Pennsylvania. All rights reserved.<br />\n# See http://www.cbica.upenn.edu/sbia/software/license.html or COPYING file.\n#\n# Contact: SBIA Group <sb... | [
[
"numpy.reshape",
"numpy.maximum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
y3sar/painter_gan | [
"374fb91927ca584b4ef3fd8ba10922c7b5201780"
] | [
"generator.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torchvision.transforms import ToTensor, ToPILImage\n\n\n\n\nclass Generator(nn.Module):\n def __init__(self):\n super().__init__()\n\n\n self.conv_block = nn.Sequential(\n\n nn.ConvTranspose2d(100, 512, 4, 1, 0),\n ... | [
[
"torch.nn.ConvTranspose2d",
"torch.randn",
"torch.nn.Tanh",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
siemens/drace | [
"2679067783b1d8f39e4c370cd72a7626ebf5f8e8"
] | [
"tools/ReportConverter/ReportConverter.py"
] | [
"# \n# ReportConverter: A graphical report generator for DRace\n# \n# Copyright 2019 Siemens AG\n# \n# Authors:\n# <Philip Harr> <philip.harr@siemens.com>\n# \n# SPDX-License-Identifier: MIT\n#\n\n## \\package ReportConverter\n## \\brief Python XML to HTML report converter for the better visualization of drace re... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.lines.Line2D",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chao1224/SGNN-EBM | [
"bda4c486e8ecb9775b635757dbe1071878be7b8a"
] | [
"src/models/SGNN_EBM_models.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch_scatter import scatter_add\n\n\nclass NCE_C_Parameter(torch.nn.Module):\n def __init__(self, N):\n super(NCE_C_Parameter, self).__init__()\n self.NCE_C = nn.Parameter(torch.zeros(N, requires_grad=True))\n\n\nclass GNN_... | [
[
"torch.LongTensor",
"torch.zeros",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.stack",
"torch.nn.ReLU",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kathoma/AutomaticKneeMRISegmentation | [
"72ea3fa96fa5de34461b5999814aa706360f4a79",
"72ea3fa96fa5de34461b5999814aa706360f4a79"
] | [
"calculate_t2.py",
"loss_functions.py"
] | [
"from __future__ import print_function, division\n\nimport sys\nsys.path.insert(0, 'lib')\nimport numpy as np\nimport random\nimport scipy.io as sio\nimport os\nimport pandas as pd\nimport scipy.ndimage as ndimage\nimport math\nimport os\nimport scipy.linalg as la\nfrom joblib import Parallel, delayed\nfrom scipy.o... | [
[
"numpy.convolve",
"numpy.ones",
"numpy.full",
"scipy.optimize.curve_fit",
"numpy.copy",
"numpy.mean",
"numpy.diff",
"numpy.array",
"numpy.exp",
"numpy.zeros",
"numpy.where"
],
[
"numpy.log",
"numpy.product",
"numpy.abs",
"numpy.reshape",
"numpy.s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
uchida-takumi/recommender_system_verification | [
"a079e0c8764926e5dc66da01a809c6ba4fde7fb7",
"a079e0c8764926e5dc66da01a809c6ba4fde7fb7",
"a079e0c8764926e5dc66da01a809c6ba4fde7fb7"
] | [
"src/module/DeepFM.py",
"src/module/knowledge_graph_attention_network/Model/utility/loader_nfm.py",
"src/module/tensorflow_DeepFM/example/main.py"
] | [
"\"\"\"\n# install the package\npip install deepctr\n\n# tutorial\nhttps://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr\n\n# github\nhttps://github.com/shenweichen/DeepCTR\n\nしかし、これは binary しか出来ないので適応不可能。\nbinary を無理矢理適応させるばあいは、非クリックデータを何らかの方法で生成する必要がある。\n\n# ---- 次のアイデア ... | [
[
"numpy.abs",
"tensorflow.Variable",
"pandas.DataFrame",
"tensorflow.global_variables_initializer",
"numpy.mean",
"numpy.random.rand",
"tensorflow.Session",
"sklearn.preprocessing.StandardScaler",
"numpy.array"
],
[
"scipy.sparse.coo_matrix",
"scipy.sparse.load_npz",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10",
"1.4",
"1.5",
... |
AlexKoff88/open_model_zoo | [
"8944a46653427cfa53db10fa91d677826adf31e1",
"8944a46653427cfa53db10fa91d677826adf31e1"
] | [
"demos/smartlab_demo/python/segmentor.py",
"demos/colorization_demo/python/colorization_demo.py"
] | [
"\"\"\"\n Copyright (C) 2021-2022 Intel Corporation\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable... | [
[
"numpy.expand_dims",
"numpy.asarray",
"numpy.concatenate",
"numpy.argmax",
"numpy.load",
"scipy.special.softmax",
"numpy.zeros"
],
[
"numpy.concatenate",
"numpy.squeeze",
"numpy.expand_dims",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
eduardojdiniz/CompNeuro | [
"20269e66540dc4e802273735c97323020ee37406"
] | [
"CichyWanderers/dataloader.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n\n# Imports\nimport h5py\nimport scipy.io as sio\nimport os\nimport requests\nimport zipfile\nimport numpy as np\nimport glob\nimport shutil\nimport pickle\n\n\ndef loadmat(matfile):\n \"\"\"Function to load .mat files.\n\n Parameters\n ----------\n matfile : str\... | [
[
"numpy.array",
"scipy.io.loadmat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
gengkunling/tensorflow_poet | [
"5ef36da08ee0f50cdaa2d08753393c549c2e75b3"
] | [
"scripts/retrain.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.framework.tensor_shape.scalar",
"tensorflow.logging.warning",
"tensorflow.python.platform.gfile.Walk",
"tensorflow.gfile.DeleteRecursively",
"tensorflow.zeros",
"tensorflow.gfile.Exists",
"tensorflow.stack",
"numpy.squeeze",
"tensorflow.cast",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
iriszero/DepthAwareCNNplus | [
"5dcc0a9279d53a2826d76631f097959d52982f8b"
] | [
"models/Deeplab.py"
] | [
"import torch.nn as nn\nimport math\nimport torch.utils.model_zoo as model_zoo\nimport torch\nfrom .base_model import BaseModel\nimport numpy as np\nfrom . import losses\nimport shutil\nfrom utils.util import *\nfrom torch.autograd import Variable\nfrom collections import OrderedDict\nfrom tensorboardX import Summa... | [
[
"torch.nn.functional.upsample",
"torch.nn.CrossEntropyLoss",
"numpy.mean",
"torch.squeeze",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
laipaang/Paddle | [
"0ec3a42e9740a5f5066053bb49a923d538eba24a",
"0ec3a42e9740a5f5066053bb49a923d538eba24a",
"0ec3a42e9740a5f5066053bb49a923d538eba24a",
"0ec3a42e9740a5f5066053bb49a923d538eba24a",
"0ec3a42e9740a5f5066053bb49a923d538eba24a"
] | [
"python/paddle/incubate/hapi/tests/test_loss.py",
"python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py",
"python/paddle/fluid/tests/unittests/test_logsumexp.py",
"python/paddle/incubate/hapi/tests/test_progressbar.py",
"python/paddle/fluid/tests/unittests/test_cast_op.py"
] | [
"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.log",
"numpy.max",
"numpy.testing.assert_allclose",
"numpy.random.uniform",
"numpy.exp",
"numpy.sum",
"numpy.random.randint"
],
[
"numpy.arange",
"numpy.zeros",
"numpy.allclose"
],
[
"numpy.random.uniform",
"numpy.exp"
],
[
"numpy.array"
],
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
YexuZhou/TimeSeriesClassification_Transformer | [
"c20e00cfac4cfdb849e57e14c184f7d424257409"
] | [
"models/embedding.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport seaborn as sns\nimport matplotlib.pylab as plt\nimport numpy as np\n\n# TODO 所有循环结构应该呈现灵活性，每一层都不能一样！\nactivation_dict = {\"relu\" : nn.ReLU,\n \"leakyrelu\" : nn.LeakyReLU,\n \"p... | [
[
"torch.cos",
"torch.sin",
"torch.zeros",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.arange",
"torch.nn.LayerNorm",
"torch.nn.MaxPool1d",
"torch.nn.Conv1d",
"matplotlib.pylab.figure",
"matplotlib.pylab.ylabel",
"torch.rand",
"torch.nn.BatchNorm2d",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
techshot25/gpytorch | [
"092d523027a844939ba85d7ea8c8c7b7511843d5",
"b4aee6f81a3428172d4914e7e0fef0e71cd1f519",
"092d523027a844939ba85d7ea8c8c7b7511843d5",
"092d523027a844939ba85d7ea8c8c7b7511843d5",
"092d523027a844939ba85d7ea8c8c7b7511843d5",
"092d523027a844939ba85d7ea8c8c7b7511843d5"
] | [
"test/kernels/test_rbf_kernel_grad.py",
"gpytorch/utils/cholesky.py",
"test/kernels/test_polynomial_kernel.py",
"gpytorch/kernels/linear_kernel.py",
"gpytorch/likelihoods/gaussian_likelihood.py",
"gpytorch/module.py"
] | [
"#!/usr/bin/env python3\n\nimport torch\nimport unittest\nfrom gpytorch.kernels import RBFKernelGrad\nfrom gpytorch.test.base_kernel_test_case import BaseKernelTestCase\n\n\nclass TestRBFKernelGrad(unittest.TestCase, BaseKernelTestCase):\n def create_kernel_no_ard(self, **kwargs):\n return RBFKernelGrad(*... | [
[
"torch.Size",
"torch.norm",
"torch.zeros",
"torch.tensor",
"torch.cuda.is_available"
],
[
"torch.cholesky",
"torch.isnan"
],
[
"torch.Size",
"torch.norm",
"torch.zeros",
"torch.tensor",
"torch.rand"
],
[
"torch.as_tensor",
"torch.is_tensor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
JamesFengi/handPose_Eric | [
"3e329181930ebc7ef0fed2abb9a9d092a8541f9c"
] | [
"lib/wyw2s_lib/make_facebank_tools/make_facebank.py"
] | [
"# make facebank\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nimport os\nimport torch\nfrom model import Backbone\nimport argparse\nfrom pathlib import Path\nfrom torchvision import transforms as trans\nfrom PIL import Image\nimport numpy as np\ndef prepare_facebank(path_images,facebank_path, model, mtcnn... | [
[
"torch.cat",
"torch.load",
"numpy.save",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.array",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
firmai/universal-portfolios | [
"b1d99d6dbcf553582d399cf3851ac4ba35a93d3e"
] | [
"universal/algo.py"
] | [
"import sys\nimport numpy as np\nimport pandas as pd\nimport itertools\nimport logging\nimport inspect\nimport copy\nfrom .result import AlgoResult, ListResult\nfrom scipy.misc import comb\nfrom . import tools\n\n\nclass Algo(object):\n \"\"\" Base class for algorithm calculating weights for online portfolio.\n ... | [
[
"numpy.log",
"pandas.Series",
"pandas.DataFrame",
"numpy.array",
"numpy.zeros",
"scipy.misc.comb"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.19",
"... |
Pandinosaurus/RandPerson | [
"1c6e935d64d8210ee4cddbf803da054016090675"
] | [
"trainCode/Source/reid/models/resmap.py"
] | [
"from __future__ import absolute_import\n\nfrom torch import nn\nimport torchvision\n\nfea_dims_small = {'layer2': 128, 'layer3': 256, 'layer4': 512}\nfea_dims = {'layer2': 512, 'layer3': 1024, 'layer4': 2048}\n\n\nclass ResNet(nn.Module):\n __factory = {\n 18: torchvision.models.resnet18,\n 34: to... | [
[
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wanirepo/Neurosynth | [
"5b770ec31c5095c16e27ebe664fa5d515c662298"
] | [
"neurosynth/analysis/reduce.py"
] | [
"import numpy as np\n\n\"\"\" Dimensional/data reduction methods. \"\"\"\n\ndef average_within_regions(dataset, img, threshold=None, remove_zero=True):\n \"\"\" Averages over all voxels within each ROI in the input image.\n\n Takes a Dataset and a Nifti image that defines distinct regions, and \n returns a numpy... | [
[
"numpy.range",
"numpy.nonzero",
"numpy.unique",
"numpy.random.shuffle",
"numpy.transpose",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jdey4/progressive-learning | [
"410b3525ab63e1f7c32e9838460b2c9af7b9d256",
"410b3525ab63e1f7c32e9838460b2c9af7b9d256",
"410b3525ab63e1f7c32e9838460b2c9af7b9d256",
"410b3525ab63e1f7c32e9838460b2c9af7b9d256"
] | [
"replaying/test.py",
"src/lifelong_dnn.py",
"replaying/plot_parity.py",
"experiments/xor_rxor_spiral_exp/main_fig_plot.py"
] | [
"#%%\nimport random\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport tensorflow.keras as keras\nimport seaborn as sns \n\nimport numpy as np\nimport pickle\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom math import log2, ceil \n\nimport sys\n#sys.path.append(\"../src/\")\nsys.path.app... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.random.seed",
"numpy.meshgrid",
"numpy.arange",
"numpy.eye",
"numpy.cumsum",
"matplotlib.pyplot.savefig",
"numpy.ones",
"numpy.concatenate",
"numpy.cos",
"numpy.std",
"numpy.sin",
"numpy.mean",
"numpy.random.uniform",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
stefan-woerner/aqua | [
"12e1b867e254977d9c5992612a7919d8fe016cb4",
"12e1b867e254977d9c5992612a7919d8fe016cb4",
"12e1b867e254977d9c5992612a7919d8fe016cb4",
"12e1b867e254977d9c5992612a7919d8fe016cb4"
] | [
"qiskit/optimization/applications/ising/knapsack.py",
"qiskit/finance/components/uncertainty_problems/european_call_delta.py",
"qiskit/chemistry/results/electronic_structure_result.py",
"qiskit/aqua/components/reciprocals/long_division.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020, 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modificatio... | [
[
"numpy.zeros",
"numpy.sum"
],
[
"numpy.ceil"
],
[
"numpy.sqrt"
],
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
theHamsta/PYRO-NN-Layers | [
"c776c3d7315f483937a7cebf667c6d491ecd57e6"
] | [
"cuda_operator.py"
] | [
"# Copyright [2019] [Christopher Syben]\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law ... | [
[
"tensorflow.test.is_built_with_cuda"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EnjoyLifeFund/macHighSierra-py36-pkgs | [
"5668b5785296b314ea1321057420bcd077dba9ea",
"5668b5785296b314ea1321057420bcd077dba9ea",
"5668b5785296b314ea1321057420bcd077dba9ea",
"5668b5785296b314ea1321057420bcd077dba9ea",
"5668b5785296b314ea1321057420bcd077dba9ea",
"5668b5785296b314ea1321057420bcd077dba9ea",
"1606c16005a5338333b4943f782f57311c6b5e4... | [
"torch/utils/model_zoo.py",
"mir_eval/segment.py",
"cvxpy_tinoco/functions/log_sum_exp.py",
"numpy-1.14.0.dev0+68a58e0-py3.6-macosx-10.13-x86_64.egg/numpy/lib/nanfunctions.py",
"astropy/io/fits/hdu/hdulist.py",
"torch/nn/init.py",
"pywt/_dwt.py",
"astropy/stats/tests/test_biweight.py",
"pydsm/delsig... | [
"import torch\n\nimport hashlib\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\nif sys.version_info[0] == 2:\n from urlparse import urlparse\n from urllib2 import urlopen\nelse:\n from urllib.request import urlopen\n from urllib.parse import urlparse\ntry:\n from tqdm import tqdm\n... | [
[
"torch.load"
],
[
"numpy.log",
"numpy.resize",
"numpy.maximum",
"numpy.sqrt",
"numpy.log2",
"numpy.unique",
"numpy.ones",
"numpy.subtract.outer",
"numpy.max",
"numpy.bincount",
"numpy.exp",
"numpy.outer",
"numpy.array",
"numpy.logical_and",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
iksteen/pyxclib | [
"2948162dd780f8230a785abfd2ee57e8ab5cc156"
] | [
"xclib/classifier/_svm.py"
] | [
"from sklearn.svm import LinearSVC\nimport numpy as np\n\n\ndef apply_threshold(data, threshold):\n data[np.where(np.abs(data) < threshold)] = 0\n\ndef train_one(data, loss, C, verbose, max_iter, threshold, dual, tol):\n def _get_features(obj):\n # Index samples iff they are required\n # Helful ... | [
[
"numpy.zeros",
"numpy.abs",
"sklearn.svm.LinearSVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isi-vista/adam | [
"91f392f2529a98cd50c095a18769ae4b55ce4292"
] | [
"adam/learner/semantics_utils.py"
] | [
"from typing import Optional, Any, Dict\n\nimport numpy as np\nimport pandas as pd\nfrom more_itertools import first\nfrom networkx import Graph, to_numpy_matrix\nimport matplotlib.pyplot as plt\nimport seaborn as sb\n\nfrom adam.semantics import Concept, KindConcept, ObjectConcept, ActionConcept\n\n\nclass Semanti... | [
[
"matplotlib.pyplot.close",
"numpy.mean",
"matplotlib.pyplot.savefig",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alexlee-gk/Theano | [
"e4e08782d3a10d010d3a99bc87fd0fc3b0465405",
"e4e08782d3a10d010d3a99bc87fd0fc3b0465405"
] | [
"theano/gpuarray/tests/test_dnn.py",
"theano/configdefaults.py"
] | [
"from __future__ import absolute_import, print_function, division\nimport logging\n\nfrom nose.plugins.skip import SkipTest\nfrom nose_parameterized import parameterized\nimport numpy\nfrom itertools import product, chain\n\nimport theano\nfrom six import StringIO\nimport theano.tensor as T\nimport theano.tests.uni... | [
[
"numpy.product",
"numpy.random.random",
"numpy.asarray",
"numpy.arange",
"numpy.random.normal",
"numpy.random.randn",
"numpy.random.rand",
"numpy.exp",
"numpy.array",
"numpy.zeros"
],
[
"numpy.distutils.system_info.get_info"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
... |
Babelscape/crocodile | [
"424ae33c68fdf22eb305e75b2f498831526d87f8"
] | [
"add_filter_relations.py"
] | [
"import jsonlines\nimport re\nimport transformers\nimport torch\nfrom tqdm import trange, tqdm\nimport argparse\nimport os, sys\n\ndef get_case_insensitive_key_value(input_dict, key):\n return next((value for dict_key, value in input_dict.items() if dict_key.lower() == key.lower()), None)\n\ndef filter_triples(m... | [
[
"torch.no_grad",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
panchiittp/pyross | [
"d5a455ae36a61e2fba29b30f1da774f1b284f1e2"
] | [
"tests/quick_test.py"
] | [
"#!python\n\"\"\"Unittesting for the pyross module. Run as python -m unittest pyross.test.\"\"\"\nimport sys\n#remove pwd from path that tries to import .pyx files\nfor i in sys.path:\n if 'pyross' in i or i == '':\n sys.path.remove(i)\n# print(sys.path)\nimport pyross\nimport unittest\nimport inspect\nim... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.asarray",
"scipy.integrate.solve_ivp",
"numpy.linalg.norm",
"numpy.ones",
"numpy.identity",
"numpy.mean",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
jhong93/vpd | [
"1ed3e8631c46e078ecb9a7756dba1f1c14aead5b",
"1ed3e8631c46e078ecb9a7756dba1f1c14aead5b"
] | [
"dummy_2d_features.py",
"vipe_dataset/keypoint.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\nConvert COCO17 2D poses to dummy embeddings for 2D-VPD.\n\"\"\"\n\nimport os\nimport argparse\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom util.io import store_pickle, load_gz_json\nfrom vipe_dataset.dataset_base import normalize_2d_skeleton\n\n\ndef get_args():\n parser ... | [
[
"numpy.array",
"numpy.mean",
"numpy.stack"
],
[
"numpy.hstack",
"numpy.random.choice",
"torch.zeros_like",
"numpy.stack",
"numpy.random.uniform",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JPompeus/Stone-Soup | [
"030c60aaf5ff92d7bb53f06e350c0bf58c9af037",
"030c60aaf5ff92d7bb53f06e350c0bf58c9af037",
"030c60aaf5ff92d7bb53f06e350c0bf58c9af037"
] | [
"stonesoup/simulator/simple.py",
"stonesoup/predictor/kalman.py",
"stonesoup/models/measurement/nonlinear.py"
] | [
"# -*- coding: utf-8 -*-\nimport datetime\n\nimport numpy as np\n\nfrom ..base import Property\nfrom ..models.measurement import MeasurementModel\nfrom ..models.transition import TransitionModel\nfrom ..reader import GroundTruthReader\nfrom ..types.detection import TrueDetection, Clutter\nfrom ..types.groundtruth i... | [
[
"numpy.sqrt",
"numpy.random.poisson",
"numpy.diff",
"numpy.random.rand",
"numpy.random.randn"
],
[
"numpy.zeros"
],
[
"numpy.linalg.inv",
"scipy.zeros",
"scipy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",... |
hello-ag/stretch_body | [
"4d9a1f10617b8f7155b8498c5333821818ce24ab"
] | [
"body/test/test_dxl_comms.py"
] | [
"# Logging level must be set before importing any stretch_body class\nimport stretch_body.robot_params\n#stretch_body.robot_params.RobotParams.set_logging_level(\"DEBUG\")\n\nimport unittest\nimport stretch_body.device\nimport stretch_body.robot as robot\nimport numpy as np\n\nclass TestTimingStats(unittest.TestCas... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jsun94/nimble | [
"e5c899a69677818b1becc58100577441e15ede13",
"e5c899a69677818b1becc58100577441e15ede13",
"e5c899a69677818b1becc58100577441e15ede13",
"e5c899a69677818b1becc58100577441e15ede13",
"e5c899a69677818b1becc58100577441e15ede13",
"e5c899a69677818b1becc58100577441e15ede13"
] | [
"benchmarks/operator_benchmark/pt/qbatchnorm_test.py",
"torch/utils/data/dataloader.py",
"torch/distributed/distributed_c10d.py",
"test/jit/test_type_sharing.py",
"torch/optim/_multi_tensor/adam.py",
"test/jit/test_with.py"
] | [
"\nimport operator_benchmark as op_bench\nimport torch\n\n\n\"\"\"Microbenchmarks for quantized batchnorm operator.\"\"\"\n\nbatchnorm_configs_short = op_bench.config_list(\n attr_names=[\"M\", \"N\", \"K\"],\n attrs=[\n [1, 256, 3136],\n ],\n cross_product_configs={\n 'device': ['cpu'],\n... | [
[
"torch.ops.quantized.batch_norm1d",
"torch.ops.quantized.batch_norm2d",
"torch.quantize_per_tensor",
"torch.rand"
],
[
"torch.empty",
"torch.cuda.current_device",
"torch.multiprocessing.get_all_start_methods",
"torch._six.queue.Queue",
"torch._C._log_api_usage_once",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
silverriver/Stylized_Dialog | [
"559dd97c4ec9c91e94deb048f789684ef3f1f9fa"
] | [
"TCFC/eval/bert_eval_acc.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may... | [
[
"torch.load",
"numpy.squeeze",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"numpy.exp",
"torch.save",
"torch.distributed.init_p... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
earthinversion/Fnet_IRIS_data_automated_download | [
"09a6e0c992662feac95744935e038d1c68539fa1",
"09a6e0c992662feac95744935e038d1c68539fa1"
] | [
"IRIS_data_download/IRIS_download_support/obspy/clients/fdsn/mass_downloader/download_helpers.py",
"IRIS_data_download/IRIS_download_support/obspy/signal/tests/test_invsim.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nHelpers for the mass downloader.\n\nIntended to simplify and stabilize the logic of the mass downloader and make\nit understandable in the first place.\n\n:copyright:\n Lion Krischer (krischer@geophysik.uni-muenchen.de), 2014-2015\n:license:\n GNU Lesse... | [
[
"numpy.argmax",
"numpy.where",
"numpy.isinf"
],
[
"matplotlib.pyplot.legend",
"numpy.allclose",
"numpy.abs",
"numpy.arange",
"numpy.sin",
"matplotlib.pyplot.plot",
"numpy.testing.assert_allclose",
"matplotlib.pyplot.show",
"numpy.sum",
"numpy.loadtxt",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tripzero/deepvoice3_pytorch | [
"90027d27dab2889d856f9db9ffaf39d4f70b3067"
] | [
"deepvoice3_pytorch/modules.py"
] | [
"# coding: utf-8\n\nimport torch\nfrom torch import nn\nimport math\nimport numpy as np\nfrom torch.nn import functional as F\n\n\ndef position_encoding_init(n_position, d_pos_vec, position_rate=1.0,\n sinusoidal=True):\n ''' Init the sinusoid position encoding table '''\n\n # keep d... | [
[
"torch.nn.functional.embedding",
"torch.sigmoid",
"torch.nn.functional.glu",
"torch.sin",
"torch.nn.functional.dropout",
"torch.nn.utils.weight_norm",
"numpy.power",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.nn.Linear",
"numpy.isscalar",
"torch.stack",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boldsort/craftassist | [
"8058d115a250e30deb60d969b7b1a5fefd6e974c"
] | [
"python/base_agent/ttad/back_translation/modeling_gpt2.py"
] | [
"# coding=utf-8\n# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy o... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.cat",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"tensorflow.train.load_variable",
"torch.nn.Linear",
"torch.matmul",
"torch.tensor",
"torch.arange",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
louis2889184/sg2im | [
"6df2095bf58703c7d6d74bf47535a7cf45690bc0"
] | [
"scripts/pl_sequence_train.py"
] | [
"import os\nimport json\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom collections import OrderedDict\n\nfrom sg2im.utils import timeit, bool_flag, LossManager\nfrom sg2im.utils import int_tuple, float_tuple, str_tuple\nfrom sg2i... | [
[
"torch.nn.functional.gumbel_softmax",
"torch.ones",
"torch.zeros",
"torch.randn",
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.utils.data.DataLoader",
"torch.matmul"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mpharrigan/OpenFermion | [
"ae5bbaed60faa019fae9d47d6e578933874e074d",
"ae5bbaed60faa019fae9d47d6e578933874e074d"
] | [
"src/openfermion/utils/_grid.py",
"src/openfermion/utils/_davidson.py"
] | [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software... | [
[
"numpy.diag",
"numpy.product",
"scipy.linalg.det",
"numpy.prod",
"scipy.linalg.inv",
"numpy.array"
],
[
"numpy.dot",
"numpy.hstack",
"numpy.abs",
"numpy.linalg.norm",
"numpy.stack",
"numpy.ones",
"numpy.real",
"numpy.linalg.eigh",
"scipy.linalg.orth"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
... |
profxj/ginga | [
"a5f447b760ac38dafa52181b3f99156545a6f2e7",
"a5f447b760ac38dafa52181b3f99156545a6f2e7",
"a5f447b760ac38dafa52181b3f99156545a6f2e7",
"a5f447b760ac38dafa52181b3f99156545a6f2e7"
] | [
"ginga/canvas/transform.py",
"ginga/qtw/CanvasRenderQt.py",
"ginga/tests/test_trcalc.py",
"ginga/canvas/types/astro.py"
] | [
"#\n# transform.py -- coordinate transforms for Ginga\n#\n# This is open-source software licensed under a BSD license.\n# Please see the file LICENSE.txt for details.\n#\nimport numpy as np\n\nfrom ginga import trcalc\nfrom ginga.misc import Bunch\n\n__all__ = ['TransformError', 'BaseTransform', 'ComposedTransform'... | [
[
"numpy.multiply",
"numpy.asarray",
"numpy.rint",
"numpy.subtract",
"numpy.add",
"numpy.divide"
],
[
"numpy.array"
],
[
"numpy.asarray",
"numpy.zeros",
"numpy.allclose"
],
[
"numpy.isfinite",
"numpy.asarray",
"numpy.arange",
"numpy.isscalar",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
vsriv90/mechanical_engineering | [
"c922cdce1a595e9acb6a87cf415fb3685caf51a3"
] | [
"Beams/Cantilever Beam - End Loaded.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# # Cantilever beams - End Loaded\n\n# \n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sn # to draw plots\n# import plotly.expr... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.text",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aetros/aetros-cli | [
"a2a1f38d6af1660e1e2680c7d413ec2aef45faab"
] | [
"aetros/utils/image.py"
] | [
"# Copyright (c) 2014-2016, NVIDIA CORPORATION. All rights reserved.\n# BSD 3-clause license\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nimport math\nfrom six.moves import range\n\n# Find the best implementation available\nfrom aetros.utils.pilutil import imresize\n\ntry:\n ... | [
[
"numpy.dot",
"numpy.minimum",
"numpy.pad",
"numpy.maximum",
"numpy.sqrt",
"numpy.ndarray",
"numpy.concatenate",
"numpy.ndindex",
"numpy.repeat",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lkeab/detectron2 | [
"d4d2948aed6c0c73558da10f8647661f61470e37",
"3a686d889ac83f722ad861be9f8754c4680561b7",
"d4d2948aed6c0c73558da10f8647661f61470e37"
] | [
"configs/Misc/torchvision_imagenet_R_50.py",
"detectron2/engine/hooks.py",
"detectron2/export/c10.py"
] | [
"\"\"\"\nAn example config file to train a ImageNet classifier with detectron2.\nModel and dataloader both come from torchvision.\nThis shows how to use detectron2 as a general engine for any new models and tasks.\nTo run, use the following command:\n\npython tools/lazyconfig_train_net.py --config-file configs/Misc... | [
[
"torch.nn.functional.cross_entropy"
],
[
"torch.autograd.profiler.profile"
],
[
"torch.ops._caffe2.BatchPermutation",
"torch.nn.functional.softmax",
"torch.full",
"torch.cat",
"torch.zeros",
"torch.ops._caffe2.GenerateProposals",
"torch.tensor",
"torch.nn.functional... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miraclestatus/mllearning | [
"f5db6642e8c05488b133ee627e5f63c92e45ff6e",
"f5db6642e8c05488b133ee627e5f63c92e45ff6e"
] | [
"ml/myscript/Logisticegression.py",
"ml/myscript/KNeighborsClassifier.py"
] | [
"import numpy as np\nfrom .metrics import accuracy_score\nclass Logisticegression():\n def __init__(self):\n # 系数\n self.coef_ = None\n # 截距\n self.intercept_ = None\n # 向量\n self._theta = None\n def _sigmoid(self, t):\n return 1./(1. + np.exp(-t))\n\n def f... | [
[
"numpy.exp",
"numpy.log",
"numpy.array",
"numpy.zeros"
],
[
"numpy.argsort",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
civodlu/trw | [
"b9a1cf045f61d6df9c65c014ef63b4048972dcdc",
"b9a1cf045f61d6df9c65c014ef63b4048972dcdc",
"b9a1cf045f61d6df9c65c014ef63b4048972dcdc",
"b9a1cf045f61d6df9c65c014ef63b4048972dcdc",
"b9a1cf045f61d6df9c65c014ef63b4048972dcdc"
] | [
"tests/test_transforms_resize_modulo_pad_crop.py",
"tutorials/classification_cifar10_resnet.py",
"tests/test_collate.py",
"src/trw/callbacks/callback_tensorboard_record_model.py",
"src/trw/callbacks/callback_reporting_classification_errors.py"
] | [
"import unittest\nimport trw\nimport torch\nimport numpy as np\n\n\nclass TestTransformsResizeModuloPadCrop(unittest.TestCase):\n def test_crop_mode_torch(self):\n batch = {\n 'images': torch.rand([2, 3, 64, 64], dtype=torch.float32)\n }\n\n tfm = trw.transforms.TransformResizeMod... | [
[
"torch.rand"
],
[
"torch.nn.Sequential",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"numpy.asarray",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.set_num_threads",
"torch.nn.BatchNorm2d"
],
[
"torch.ones",
"numpy.ones"
],
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
djhoese/verde | [
"ad14acf94717ee5c6672559f40576f65989753a5",
"ad14acf94717ee5c6672559f40576f65989753a5",
"ad14acf94717ee5c6672559f40576f65989753a5",
"24416cfc8388c6c7f3c0867dcd2ad3fdca37bb1b"
] | [
"verde/tests/test_scipy.py",
"verde/datasets/sample_data.py",
"verde/tests/test_coordinates.py",
"verde/scipygridder.py"
] | [
"\"\"\"\nTest the scipy based interpolator.\n\"\"\"\nimport warnings\n\nimport pytest\nimport pandas as pd\nimport numpy as np\nimport numpy.testing as npt\n\nfrom ..scipygridder import ScipyGridder\nfrom ..coordinates import grid_coordinates\nfrom ..datasets.synthetic import CheckerBoard\n\n\ndef test_scipy_gridde... | [
[
"numpy.ones_like",
"numpy.testing.assert_allclose"
],
[
"numpy.arange",
"pandas.read_csv"
],
[
"numpy.testing.assert_allclose"
],
[
"numpy.ravel",
"sklearn.utils.validation.check_is_fitted"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
liushulinle/CRACSpell | [
"e0b495ed8424be7fdbd7fc3ef8c2919ab195b0e4"
] | [
"src/run_evaluation.py"
] | [
"import sys, os\nimport numpy as np\nimport tensorflow as tf\nfrom bert_tagging import DataProcessor, BertTagging\nimport modeling\nimport optimization\nimport time\nfrom tagging_eval import score_f\ntf.logging.set_verbosity(tf.logging.ERROR)\n\nDEBUG = False\ndef evaluate(FLAGS, label_list=None):\n gpuid = FLAG... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.ConfigProto",
"tensorflow.logging.set_verbosity",
"tensorflow.Session",
"tensorflow.train.Saver"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
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