repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
wq13552463699/UCD_UR5E | [
"513acb7e235ab940fd03c3038208678e285690f3"
] | [
"Simulation/reaching_gym/DQN_train.py"
] | [
"from collections import deque\nimport tensorflow as tf\nfrom DQN_agent import DQNAgent\nimport numpy as np\nimport random\nfrom rl_symbol_env import symbol_env\n\n# setting seeds for result reproducibility. This is not super important\nrandom.seed(2212)\nnp.random.seed(2212)\ntf.set_random_seed(2212)\n\n# Hyperpar... | [
[
"numpy.amax",
"numpy.expand_dims",
"numpy.random.seed",
"tensorflow.Session",
"tensorflow.set_random_seed",
"numpy.random.uniform",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
lindehesse/M3d-Cam | [
"c5e709ff7e9a9805333bc1131bb54f252ac0bbd0"
] | [
"medcam/backends/guided_backpropagation.py"
] | [
"import torch\nfrom torch import nn\nfrom medcam.backends.base import _BaseWrapper\n\n\nclass GuidedBackPropagation(_BaseWrapper):\n\n def __init__(self, model, postprocessor=None, retain_graph=False):\n \"\"\"\n \"Striving for Simplicity: the All Convolutional Net\"\n https://arxiv.org/pdf/... | [
[
"torch.mean",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isVoid/cudf | [
"1a3b3f217be93a55b47af3a9d0da29f0fcb7c7e9",
"1a3b3f217be93a55b47af3a9d0da29f0fcb7c7e9"
] | [
"python/dask_cudf/dask_cudf/core.py",
"python/dask_cudf/dask_cudf/tests/test_join.py"
] | [
"# Copyright (c) 2018-2020, NVIDIA CORPORATION.\nimport warnings\nfrom distutils.version import LooseVersion\n\nimport numpy as np\nimport pandas as pd\nfrom tlz import partition_all\n\nimport dask\nfrom dask import dataframe as dd\nfrom dask.base import normalize_token, tokenize\nfrom dask.compatibility import app... | [
[
"numpy.float64"
],
[
"numpy.random.seed",
"numpy.asarray",
"numpy.arange",
"numpy.testing.assert_array_equal",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Daniel-Vital/magicroot | [
"5dba2fa23d8933946af1c2e4244f0e18e455721f"
] | [
"src/magicroot/df/create.py"
] | [
"import numpy as np\nimport pandas as pd\n\n\ndef empty(shape, *args, **kwargs):\n canvas = np.empty(shape)\n canvas[:] = np.NaN\n return pd.DataFrame(canvas, *args, **kwargs)\n\n\ndef const_col(df, const, *args, **kwargs):\n s = pd.Series(np.ones(len(df)) * const, *args, **kwargs)\n s.index = df.ind... | [
[
"numpy.empty",
"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": []
}
] |
G-Wang/Pytorch-Voice-Loop | [
"9d2d5fdebc2e5be06e8a2809e731a8ea378330ea"
] | [
"test.py"
] | [
"import torch\nfrom model import Loop\nfrom hparams import Hparams\nfrom utils import VCTKDataSet, my_collate_fn\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\ndef no_test_train():\n hp = Hparams()\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n model = L... | [
[
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
moinnadeem/apex | [
"f5bdd8e4e2a8d4c31c3b2bfe4662953d6903da2a"
] | [
"tests/L0/run_transformer/test_transformer_module.py"
] | [
"from typing import Tuple\nimport os\nimport subprocess\nimport sys\nimport unittest\n\n\nSEVERALGPU_TEST = [\n \"bert_minimal_test\",\n \"gpt_minimal_test\",\n \"dynamic_batchsize_test\",\n]\n\n\ndef get_multigpu_launch_option(min_gpu):\n should_skip = False\n import torch\n\n num_devices = torch... | [
[
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Crazyalltnt/RL-Alogorithms-Implement | [
"27905f1c1890b1aff907564230b4ec0c22e60ba0"
] | [
"Independent/DDPG/env.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n'''\n@Author: John\n@Email: johnjim0816@gmail.com\n@Date: 2020-06-10 15:28:30\n@LastEditor: John\nLastEditTime: 2021-09-16 00:52:30\n@Discription: \n@Environment: python 3.7.7\n'''\n\nimport gym\nimport numpy as np\n\n\nclass NormalizedActions(gym.ActionWrapper):\n ''' 将 a... | [
[
"numpy.ones",
"numpy.random.randn",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Antrovirens/learn-surveying-software-designing | [
"96b492510b3a3ac970675ddffb25d2b4d2b9970c"
] | [
"读取rinex文件/MainWindow.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nModule implementing MainWindow.\n\"\"\"\nimport sys\nimport numpy as np\n#from math import pi, atan, sqrt\n#from datetime import datetime\n\n#import matplotlib\n#matplotlib.use(\"Qt5Agg\") # 声明使用QT5\n#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanv... | [
[
"numpy.set_printoptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yiyuezhuo/loss-landscape | [
"464dffdeba6cd45f0275836552e551addc7249a2"
] | [
"cifar10/model_loader.py"
] | [
"import os\nimport torch, torchvision\nimport cifar10.models.vgg as vgg\nimport cifar10.models.resnet as resnet\nimport cifar10.models.densenet as densenet\n\n# map between model name and function\nmodels = {\n 'vgg9' : vgg.VGG9,\n 'densenet121' : densenet.DenseNet121,\n 'resnet1... | [
[
"torch.nn.DataParallel",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akrajewska/cost-prophet | [
"f31b462b144c76ccfd56153e156d08d0795b579d"
] | [
"cost_prophet/utils/experiment.py"
] | [
"import os\n\nimport pandas as pd\nfrom dask import delayed, compute\nfrom dotenv import dotenv_values\n\nfrom cost_prophet.utils.evaluation import test_error\nfrom cost_prophet.utils.linear_alebra import get_known_indices, split_tests_sets\n\nconfig = dotenv_values()\n\nOUTPUT_DIR = config.get(\"OUTPUT_DIR\")\n\n\... | [
[
"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": []
}
] |
rachel-1/transformers | [
"d7398397c8202b8aa4bceaff51e90c6d12c1dec1"
] | [
"transformers/modeling_bert.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.nn.Softmax",
"torch.nn.functional.softmax",
"torch.mean",
"torch.zeros",
"torch.nn.Embedding",
"torch.pow",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.from_numpy",
"torch.nn.functional.sigmoid",
"torch.arange",
"tensorflow.train... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
reenasheoran/Flight_Fare_MLOP | [
"6848893c253307b1bc76f50d00016a458cdad1df"
] | [
"src/split_data.py"
] | [
"import os\nimport argparse\nfrom get_data import read_params\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ndef split_data(config_path):\n config = read_params(config_path)\n data_path = config[\"filter_data\"][\"filter_data_csv\"]\n df = pd.read_csv(data_path,sep=',',encodi... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nyu-mll/CoLA-baselines | [
"dd095d3646ed05a315280aaa8ed4ec84ba435b3e"
] | [
"acceptability/modules/lm_generator.py"
] | [
"import argparse\nimport torch\nimport os\n\nfrom torch.autograd import Variable\n\nfrom .dataset import Vocab\nfrom acceptability.utils import get_lm_generator_parser, seed_torch\n\n\nclass LMGenerator():\n def __init__(self):\n parser = get_lm_generator_parser()\n self.args = parser.parse_args()\... | [
[
"torch.LongTensor",
"torch.multinomial",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiaowanzi123good/adversarial-polyglots | [
"fa5000acdf0a49c2373d17c9133e8670385cb855"
] | [
"attacks/run_polygloss_nli.py"
] | [
"from transformers import glue_processors as processors\nimport csv, os, argparse, ray, torch, time, json\nfrom pathlib import Path\nfrom polygloss import PolyglossPairSequenceClassificationHF\nfrom tqdm import tqdm\nfrom ray.util import ActorPool\nfrom math import ceil\n\nparser = argparse.ArgumentParser()\nparser... | [
[
"torch.cuda.device_count",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kunakl07/AL-MLresearch | [
"5ab80169563e6cbe4de15aefa4bbfa09298795f9"
] | [
"src/active_learning_script/active_learning.py"
] | [
"import shutil\nimport os\nimport logging\nimport argparse\nfrom keras.preprocessing import image\nimport numpy as np\nfrom keras.preprocessing.image import img_to_array\nimport tensorflow as tf\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef predict(model_path, test_path):... | [
[
"tensorflow.keras.models.load_model",
"numpy.expand_dims",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
georgedeath/eshotgun | [
"6b34c9acfd1d56ebc64493babd77799441298d31"
] | [
"eshotgun/test_problems/util.py"
] | [
"import numpy as np\n\n\n# simple wrapper around functions to act as though they reside in [0, 1]^d\nclass uniform_problem_wrapper():\n def __init__(self, problem):\n self.problem = problem\n self.dim = problem.dim\n\n self.real_lb = problem.lb\n self.real_ub = problem.ub\n\n s... | [
[
"numpy.atleast_2d",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dirac231/BCHamming | [
"0fedd3b909a6f8a876547b0e368b817664aac212"
] | [
"REED_SOLOMON/RS-noise.py"
] | [
"from qiskit import *\nfrom unireedsolomon import *\nfrom matplotlib import *\nfrom math import *\nfrom collections import defaultdict\nimport numpy as np\nfrom numpy.polynomial import Polynomial\nfrom qiskit.providers.aer import AerSimulator\nfrom qiskit.circuit.library import QFT\nfrom qiskit.visualization import... | [
[
"numpy.polynomial.Polynomial",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
weinajin/evaluate_multimodal_medical_image_heatmap_explanation | [
"c022613ea05818c842e0760c44a0a2cb9cc0c424"
] | [
"code/model/loss.py"
] | [
"import torch.nn.functional as F\nimport torch\n\ndef nll_loss(output, target):\n return F.nll_loss(output, target)\n\n\ndef ce_loss(output, target):\n return F.cross_entropy(output, target)\n\ndef ce_loss_weighted(output, target):\n weighted_loss = torch.tensor([0.34, 0.66])\n return F.cross_entropy(ou... | [
[
"torch.nn.functional.nll_loss",
"torch.nn.functional.cross_entropy",
"torch.tensor",
"torch.exp",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faradaymahe/DynaPhopy | [
"e1201f6de62b4303c68a7808ed19175364409586"
] | [
"dynaphopy/interface/iofile/trajectory_parsers.py"
] | [
"import os\nimport numpy as np\nimport mmap\nimport dynaphopy.dynamics as dyn\nimport warnings\n\n\n# VASP OUTCAR file parser\ndef read_vasp_trajectory(file_name, structure=None, time_step=None,\n limit_number_steps=10000000, # Maximum number of steps read (for security)\n ... | [
[
"numpy.dot",
"numpy.min",
"numpy.linalg.inv",
"numpy.arccos",
"numpy.linalg.norm",
"numpy.max",
"numpy.argsort",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
littlePrince126/HRNet_Pose_Estimation_TensorFlow2 | [
"7c7ebbb7eb642dbdecddb0adebfbce01c027984b"
] | [
"utils/tools.py"
] | [
"from configuration.coco_config.w32_256x192_config import CocoW32Size256x192\nimport tensorflow as tf\n\n\ndef get_config_params(config_name):\n if config_name == \"coco_w32_256x192\":\n config_params = CocoW32Size256x192()\n return config_params\n else:\n raise ValueError(\"Invalid confi... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.image.crop_to_bounding_box",
"tensorflow.random.uniform",
"tensorflow.io.decode_image",
"tensorflow.image.resize",
"tensorflow.io.read_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
khaledsabry97/Argus | [
"c794f6e46ec529a836db127dfdb33b3161cf79ee"
] | [
"Car_Detection_TF/yolo3/model.py"
] | [
"\"\"\"YOLO_v3 Model Defined in Keras.\"\"\"\n\nfrom functools import wraps\n\nimport numpy as np\nimport tensorflow as tf\nfrom keras import backend as K\nfrom keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom kera... | [
[
"tensorflow.boolean_mask",
"numpy.expand_dims",
"numpy.maximum",
"numpy.minimum",
"tensorflow.image.non_max_suppression",
"numpy.argmax",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ckkelvinchan/mmgeneration | [
"198e7e2112efd38c32c5b2837a46b9ff6badabb2"
] | [
"mmgen/core/evaluation/metrics.py"
] | [
"import os\nimport pickle\nfrom abc import ABC, abstractmethod\n\nimport mmcv\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn.functional as F\nfrom mmcv.runner import get_dist_info\nfrom scipy import linalg, signal\nfrom scipy.stats import entropy\nfrom torchvision import models... | [
[
"torch.abs",
"torch.jit.load",
"torch.mean",
"torch.nn.functional.softmax",
"torch.cat",
"numpy.asarray",
"torch.zeros",
"torch.sum",
"numpy.concatenate",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"numpy.iscomplexobj",
"torch.nn.functional.in... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
bonlime/pytorch-tools | [
"46fceaba823a613171efdb91436a8357b492af5e"
] | [
"pytorch_tools/optim/adamp.py"
] | [
"\"\"\"\nAdamP\nCopyright (c) 2020-present NAVER Corp.\nMIT license\n\n+ eps inside sqrt\n+ init sq with ones instead of zeros\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim.optimizer import Optimizer, required\nimport math\n\n\nclass AdamP(Optimizer):\n def __i... | [
[
"torch.zeros_like",
"torch.nn.functional.cosine_similarity",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
belac626/AeroPy | [
"4f045306427e08b742237b7393ce9602f1072d60",
"4f045306427e08b742237b7393ce9602f1072d60"
] | [
"aeropy/geometry/test_inflections.py",
"aeropy/geometry/other_methods.py"
] | [
"from __future__ import print_function\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimport aeropy.xfoil_module as xf\r\nfrom aeropy.CST.module_2D import *\r\nfrom aeropy.aero_module import Reynolds\r\nfrom aeropy.airfoil_module import CST, create_x\r\n\r\nAu = [0.23993240191629417, 0.344682271389... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"numpy.linspace",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"matplotlib.pyplot.legend",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leandro-santiago/bloomwisard | [
"4c02610c4ef2d2cf8424797c8a815da182ca2383"
] | [
"experiment/diabetes/overall_info.py"
] | [
"import numpy as np\nimport math\nimport sys\nfrom timeit import default_timer as timer\nsys.path.append(\"../../\") \nfrom core import wnn\nfrom encoding import thermometer\nfrom encoding import util\n\n#Load Diabetes data\nbase_path = \"../../dataset/diabetes/\"\n\n#2/3 Test\nbits_encoding = 20\ntrain_data, train... | [
[
"numpy.std",
"numpy.append",
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gopalakrishna-r/tensorpack | [
"3aa99653d1201a49f6be0542a764d335153e84f6"
] | [
"tensorpack/contrib/keras.py"
] | [
"# -*- coding: utf-8 -*-\n# File: keras.py\n\nfrom contextlib import contextmanager\nimport six\nimport tensorflow as tf\nfrom tensorflow import keras\n\nfrom ..callbacks import Callback, CallbackToHook, InferenceRunner, InferenceRunnerBase, ScalarStats\nfrom ..models.regularize import regularize_cost_from_collecti... | [
[
"tensorflow.keras.metrics.get",
"tensorflow.reduce_mean",
"tensorflow.keras.backend.learning_phase",
"tensorflow.trainable_variables",
"tensorflow.add_to_collection",
"tensorflow.keras.losses.get",
"tensorflow.name_scope",
"tensorflow.compat.v1.get_variable_scope",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
kuzn137/DisasterResponsePipeline | [
"d2b87410bcd41210a25eb5b533f78f3c3cbe0fe6"
] | [
"models/train_classifier.py"
] | [
"import sys\n# import libraries\nimport nltk\nimport pickle\nfrom sklearn.base import BaseEstimator, TransformerMixin\nnltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger'])\nfrom sklearn.model_selection import GridSearchCV\nimport nltk\nnltk.download('punkt') \nnltk.download('stopwords')\n# import libr... | [
[
"sklearn.model_selection.GridSearchCV",
"sklearn.ensemble.RandomForestClassifier",
"numpy.unique",
"sklearn.model_selection.train_test_split",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.feature_extraction.text.TfidfTransformer",
"sklearn.metrics.classification_report",... | [
{
"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": []
}
] |
lalonderodney/INN-Inflated-Neural-Nets | [
"50ce42e4584815d066d0fd39a7f12f55130910e5"
] | [
"custom_data_aug.py"
] | [
"'''\nINN: Inflated Neural Networks for IPMN Diagnosis\nOriginal Paper by Rodney LaLonde, Irene Tanner, Katerina Nikiforaki, Georgios Z. Papadakis, Pujan Kandel,\nCandice W. Bolan, Michael B. Wallace, Ulas Bagci\n(https://link.springer.com/chapter/10.1007/978-3-030-32254-0_12, https://arxiv.org/abs/1804.04241)\nCod... | [
[
"numpy.reshape",
"numpy.arange",
"scipy.ndimage.interpolation.map_coordinates",
"numpy.ceil",
"numpy.zeros_like",
"numpy.float32",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
gilwoolee/brl_gym | [
"9c0784e9928f12d2ee0528c79a533202d3afb640"
] | [
"brl_gym/envs/mujoco/maze_continuous.py"
] | [
"import numpy as np\nfrom gym import utils\nfrom gym.spaces import Box\nfrom gym.envs.mujoco import mujoco_env\nfrom mujoco_py import MjViewer\nimport os\nasset_dir = \"/home/gilwoo/Workspace/brl_gym/brl_gym/envs/mujoco/\"\n\n# Goal can be anywhere\nclass MazeContinuous(mujoco_env.MujocoEnv, utils.EzPickle):\n d... | [
[
"numpy.linalg.norm",
"numpy.ones",
"numpy.concatenate",
"numpy.random.normal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lcontento/cytokit | [
"168c7ebd598664f0833d26078b167456ce4a899d"
] | [
"pub/analysis/mc38-spheroid/spheroid_cytometer.py"
] | [
"import numpy as np\nfrom skimage import util\nfrom skimage import draw\nfrom skimage import filters\nfrom skimage import measure\nfrom skimage import exposure\nfrom skimage import transform\nfrom skimage import feature\nfrom skimage import morphology\nfrom skimage import segmentation\nfrom skimage import img_as_fl... | [
[
"numpy.hstack",
"numpy.log",
"scipy.ndimage.gaussian_filter",
"scipy.ndimage.morphology.binary_fill_holes",
"numpy.asarray",
"scipy.ndimage.median_filter",
"scipy.ndimage.distance_transform_edt",
"numpy.stack",
"numpy.full",
"numpy.ones",
"scipy.ndimage.generic_gradient... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
andrfish/tensorflow-alexnet | [
"4c44e9a0ec90ec4731775a2d94415d2b5727f34d"
] | [
"simple_kaggle_mnist_alexnet.py"
] | [
"\"\"\"\nThis is simple Alexnet train implementation modified for Kaggle mnist data.\n\"\"\"\n\nimport time\nimport tensorflow as tf\n\nimport logging\ntf.get_logger().setLevel(logging.ERROR)\n\nimport kaggle_mnist_input as loader\nimport os\nimport csv\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_integer('... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.nn.max_pool",
"tensorflow.nn.l2_loss",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.app.flags.DEFINE_boolean",
"tensorflow.nn.conv2d",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.initialize_all_variables",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
qdev-dk/publications | [
"b46b51b0b267d6aab3edc043ab0a09cad61aaca4"
] | [
"main.py"
] | [
"import arxivtodf\nimport dftohtml\nimport pandas as pd\n\nname_file_path = '/var/projects/qdev/names.xlsx'\nnames_df = pd.read_excel(name_file_path, dtype={'names':str, 'fullnames':str, 'search_queries':str, 'homepageids':str})\n\nsearch_queries = names_df['search_queries'].to_list()\nnames = names_df['names'].to... | [
[
"pandas.merge",
"pandas.read_excel"
]
] | [
{
"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": [],
"tensorflow": []
}
] |
maxuanquang/cc | [
"c6279d27ce51970adec58b6952c50b123acd6ff4"
] | [
"data/prepare_train_data_custom.py"
] | [
"from __future__ import division\nimport argparse\nimport scipy.misc\nimport numpy as np\nfrom joblib import Parallel, delayed\nfrom tqdm import tqdm\nfrom path import Path\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"dataset_dir\", metavar='DIR',\n help='path to original dataset'... | [
[
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MattyBoy4444/home-assistant | [
"b7b8296c73b28634bb9c60efe1ad976b1092fce8"
] | [
"homeassistant/components/binary_sensor/trend.py"
] | [
"\"\"\"\nA sensor that monitors trends in other components.\n\nFor more details about this platform, please refer to the documentation at\nhttps://home-assistant.io/components/sensor.trend/\n\"\"\"\nfrom collections import deque\nimport logging\nimport math\n\nimport voluptuous as vol\n\nfrom homeassistant.componen... | [
[
"numpy.polyfit",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DanialTaheri/KATRec | [
"a54d40196c25a09796665521cf482ba4eada072d"
] | [
"Model/utility/load_data.py"
] | [
"'''\nCreated on Dec 18, 2018\nTensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:\nWang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.\n@author: Xiang Wang (xiangwang@u.nus.edu)\n'''\nimport collections\nimport numpy as np\nimport random as rd\n\nc... | [
[
"numpy.array",
"numpy.random.randint",
"numpy.loadtxt",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ivarlokhorst/dask-geomodeling | [
"392aac0d7ee271c9e817e9654cf178f75ae76265"
] | [
"dask_geomodeling/geometry/aggregate.py"
] | [
"\"\"\"\nModule containing raster blocks that aggregate rasters.\n\"\"\"\nfrom math import ceil, floor, log, sqrt\nfrom collections import defaultdict\nfrom functools import partial\nimport warnings\n\nfrom scipy import ndimage\nimport numpy as np\nimport geopandas as gpd\n\nfrom dask import config\nfrom dask_geomo... | [
[
"numpy.all",
"numpy.isnan",
"numpy.isfinite"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TencentYoutuResearch/Pruning-PFF | [
"0ed6a266cfbfdc1858ce475b3b1e78b18eb5cd2b"
] | [
"models/vgg.py"
] | [
"import torch\nimport torch.nn as nn\nfrom .stripe import *\n\n__all__ = ['VGG']\ndefault_cfg = {\n 'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n 'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n 'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 2... | [
[
"torch.nn.Sequential",
"torch.ones",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hotchya/basic-pytorch | [
"82dca4ee8ccc3596a58d2573f914c756e8740ed9"
] | [
"main.py"
] | [
"import os\nimport argparse\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\n\nimport models\n\ndef save_model(model):\n state = {\n 'acc' : args.acc,\n 'model_state_dict': model... | [
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.manual_seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bmathias12/model-analysis | [
"87bedbcf817557a40aaaf7bad614763f8713e5b3"
] | [
"binaryclassifier/plotting/prediction_density.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\n\ndef plot_prediction_density(\n y_true, scores, figsize=(8,5),\n title='Prediction Density Plot',\n colors=['red', 'blue']):\n\n class_set = sorted(set(y_true))\n\n x_grid = np.linspace(0, 1, 1000... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"scipy.stats.gaussian_kde",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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"... |
waddupitzme/graph-neural-pde | [
"004a30c9e838866ac8b78d14b7414224a24014a5"
] | [
"src/function_transformer_attention.py"
] | [
"import torch\nfrom torch import nn\nfrom torch_geometric.utils import softmax\nimport torch_sparse\nfrom torch_geometric.utils.loop import add_remaining_self_loops\nimport numpy as np\nfrom data import get_dataset\nfrom utils import MaxNFEException, squareplus\nfrom base_classes import ODEFunc\n\n\nclass ODEFuncTr... | [
[
"torch.mean",
"torch.sigmoid",
"torch.ones",
"numpy.sqrt",
"torch.cat",
"torch.nn.init.constant_",
"torch.sum",
"torch.nn.CosineSimilarity",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ScripteJunkie/T3 | [
"f0f205f39bf3dc23c2dc13d0037fbae6ac296874"
] | [
"python/HSVImageCalibration.py"
] | [
"import cv2\nimport numpy as np\n\ndef nothing(x):\n pass\n\n# Load image\nimage = cv2.imread('/python/Unused Images/Screenshot 2022-04-02 01-28-33.png')\n\nwidth = int(image.shape[1] / 2)\nheight = int(image.shape[0] / 2)\ndim = (width, height)\n\nimage = cv2.resize(image, dim)\n\n# Create a window\ncv2.namedWi... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
azzatha/RTG-Simulation-tool | [
"b9001876e79d2e0c5936df073fd16dec4c52e4a8"
] | [
"calculate_prob_cdf.py"
] | [
"import pandas as pd\nfrom sklearn import preprocessing\nimport numpy as np\nimport glob\nimport math\n\n# Function to Convert the cM to probability of recombination\ndef probability (genaticMaps):\n prob = (1.0 - math.exp(-genaticMaps[\"cM\"] / 50)) / 2.0\n return prob\n\n\n# Read the files in the current direct... | [
[
"sklearn.preprocessing.normalize",
"pandas.read_csv",
"numpy.cumsum",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
pchaos/quanttesting | [
"98331670547e8a45ba93b49f3e9c660495645114"
] | [
"testing/examples/talib-macd.py"
] | [
"'''\nTa-lib计算MACD\n'''\nimport pandas as pd\nimport numpy as np\nimport talib as ta\nimport tushare as ts\nfrom matplotlib import rc\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nrc('mathtext', default='regular')\nsns.set_style('white')\n# %matplotlib\nplt.rcParams[\"figure.figsize\"] = (20, 10)\n\ndw... | [
[
"matplotlib.pyplot.show",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fountain-y/ranksortloss | [
"e9c4ba360b2dd905babb0ed1a445fca588c5ecec"
] | [
"mmdet/core/evaluation/mean_ap.py"
] | [
"from multiprocessing import Pool\n\nimport mmcv\nimport numpy as np\nfrom mmcv.utils import print_log\nfrom terminaltables import AsciiTable\n\nfrom .bbox_overlaps import bbox_overlaps\nfrom .class_names import get_classes\n\nimport pandas as pd\n\n\ndef average_precision(recalls, precisions, mode='area'):\n \"... | [
[
"numpy.hstack",
"numpy.minimum",
"numpy.maximum",
"numpy.arange",
"numpy.cumsum",
"pandas.DataFrame",
"numpy.ones",
"numpy.finfo",
"numpy.empty",
"numpy.zeros_like",
"numpy.any",
"numpy.mean",
"numpy.where",
"numpy.argsort",
"numpy.array",
"numpy.zer... | [
{
"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": []
}
] |
dilberdillu/graphcu | [
"eaf05d1b287beab594a48c2b2a777f60a5815b4b"
] | [
"graphcu/graph.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 19 15:06:21 2018\n\n@author: dillu\n\"\"\"\n\nfrom networkx.exception import NetworkXError\nimport matplotlib.pyplot as plt\n\nclass Graph(object):\n \n node_dict_factory = dict\n node_attr_dict_factory = dict\n adjlist_outer_d... | [
[
"matplotlib.pyplot.draw"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
avinesh09/mnist-deepml | [
"ffbdd2d58e47d704d7a05aaf6e109ac56a15a99d"
] | [
"mnist-saved-model.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 appl... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.FixedLenFeature",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.summary.scalar",
"tensorflow.to_int64",
"tensorflow.Graph",
"numpy.reshape",
"tensorflow.app.flags.DEFINE_integer",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
KalcMatej99/DQN-Trading | [
"cc28827fded23e088fd524d7989f12c38dbbe06f"
] | [
"Main.py"
] | [
"# Importing DataLoaders for each model. These models include rule-based, vanilla DQN and encoder-decoder DQN.\nfrom tokenize import String\nfrom xmlrpc.client import Boolean\nfrom DataLoader.DataLoader import YahooFinanceDataLoader\nfrom DataLoader.DataForPatternBasedAgent import DataForPatternBasedAgent\nfrom Dat... | [
[
"matplotlib.pyplot.legend",
"torch.cuda.is_available",
"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": []
}
] |
dHannasch/beluga | [
"519e1ca2a43a86bc47737c45484288b2bacc1338"
] | [
"examples/Astrodynamics/Detumble/plotresults.py"
] | [
"from beluga.utils import load\nimport matplotlib.pyplot as plt\n\ndata = load('data.blg')\nsol_set = data['solutions']\n\nsol = sol_set[-1][-1]\n\nplt.figure()\nplt.plot(sol.t, sol.y[:,0], color='b', label='$\\omega_1$')\nplt.plot(sol.t, sol.y[:,1], color='r', label='$\\omega_2$')\nplt.plot(sol.t, sol.y[:,2], colo... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sethdp/sethdp.github.io | [
"dc4e871f669db37df8bad890fc817f2764a96d55"
] | [
"assets/blog/dmd/dmd.py"
] | [
"# %%\nimport math\nimport matplotlib as mpl\nimport matplotlib.animation as animation\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import ImageGrid\nfrom mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable\nimport numpy as np\nimport pydmd\n\nmpl.rcParams.update({\n 'figure.dpi': 2... | [
[
"numpy.ndarray",
"numpy.fft.fftshift",
"numpy.argmin",
"numpy.vander",
"numpy.arange",
"numpy.sin",
"matplotlib.pyplot.Circle",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.log",
"numpy.fft.ifft",
"matplotlib.rcParams.update",
"matplotlib.animation.FuncAnim... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kikacaty/c_study | [
"b0556603f638f616a75cc8460c424eb002904549"
] | [
"eval_ensemble_arch.py"
] | [
"'''\nDescription: \nAutor: Jiachen Sun\nDate: 2021-08-05 13:18:39\nLastEditors: Jiachen Sun\nLastEditTime: 2021-08-05 14:10:13\n'''\nfrom __future__ import print_function\nimport os\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.optim... | [
[
"torch.max",
"numpy.random.seed",
"torch.cuda.manual_seed",
"torch.cuda.current_device",
"torch.manual_seed",
"torch.cuda.device_count",
"torch.stack",
"numpy.concatenate",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"torch.nn.DataParall... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aaronsnoswell/UniModal-IRL | [
"02ea22b47d50ff1234c25b2293db01162112d534"
] | [
"unimodal_irl/sw_maxent_irl.py"
] | [
"\"\"\"Implements Exact Maximum Entropy IRL from my thesis\"\"\"\n\nimport numpy as np\nfrom numba import jit\nfrom numba import types\nfrom numba.typed import Dict, List\n\nfrom scipy.optimize import minimize\n\n\nfrom mdp_extras import (\n Linear,\n Disjoint,\n trajectory_reward,\n DiscreteExplicitExt... | [
[
"numpy.log",
"numpy.ones_like",
"numpy.arange",
"numpy.finfo",
"numpy.isneginf",
"numpy.max",
"numpy.argmax",
"numpy.zeros_like",
"numpy.errstate",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rakshithb/FFF-Sensing | [
"13ee0724e97b0142766cd858102b5cce00c798cd"
] | [
"FFF_SysID_Vision_Analysis.py"
] | [
"# import required libraries\r\nimport numpy as np\r\nimport cv2\r\nprint('OpenCV version: '+cv2.__version__)\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport datetime\r\nimport os\r\nfrom collections import Counter\r\n\r\n# Set source folder\r\nSRC_FOLDER = \"C:/Users/raksh/OneDrive - The Pennsy... | [
[
"numpy.hstack",
"pandas.read_excel",
"numpy.min",
"pandas.DataFrame",
"numpy.argwhere",
"numpy.ones",
"numpy.max",
"numpy.copy",
"numpy.mean",
"numpy.float32",
"pandas.ExcelWriter",
"numpy.zeros"
]
] | [
{
"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": [],
"tensorflow": []
}
] |
heroclass728/train_object_detection_model | [
"f2346c72b0d89eb740881d01b0ff6bae0759ba10"
] | [
"object_detection/trainer.py"
] | [
"# Copyright 2017 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.concat",
"tensorflow.control_dependencies",
"tensorflow.cast",
"tensorflow.group",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"tensorflow.losses.get_total_loss",
"tensorflow.get_collection",
"tensorflow.losses.get_losses",
"tensorflow.check_numerics",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matthuszagh/fmcw | [
"b60f18102a948562bfc954de2658ba5fb48c3c79"
] | [
"simulations/openems/linear_taper.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nTest the effect of different linear taper angles/lengths on S11 and S21.\n\"\"\"\n\nimport numpy as np\nfrom pyems.pcb import common_pcbs\nfrom pyems.simulation import Simulation\nfrom pyems.utilities import print_table\nfrom pyems.structure import PCB, Microstrip, Taper\nfrom pyems.... | [
[
"numpy.tan",
"numpy.arange",
"numpy.concatenate",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
google/graph-gen | [
"6481e09696ee5b29c12d893a61c27b80f8d874be"
] | [
"research/graph_gen/real_NVP.py"
] | [
"# Copyright 2018 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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed t... | [
[
"tensorflow.get_variable",
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.zeros",
"tensorflow.layers.fully_connected",
"tensorflow.reduce_sum",
"tensorflow.nn.moments",
"tensorflow.squeeze",
"tensorflow.matmul",
"tensorflow.Print",
"tensorflow.nn.si... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
zysszy/Recoder | [
"f57736db376b6b24e755fcfbd1ce232c52aa31af"
] | [
"ConvolutionForward.py"
] | [
"import torch.nn as nn\nfrom gelu import GELU\nclass ConvolutionLayer(nn.Module):\n def __init__(self, dmodel, layernum, kernelsize=3, dropout=0.1):\n super(ConvolutionLayer, self).__init__()\n self.conv1 = nn.Conv1d(dmodel, layernum, kernelsize, padding=(kernelsize-1)//2)\n self.conv2 = nn.... | [
[
"torch.nn.Dropout",
"torch.nn.Conv1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aydevosotros/pandas | [
"9444dce96954c546333d5aecc92a06c3bfd19aa5"
] | [
"pandas/tests/series/indexing/test_loc.py"
] | [
"# coding=utf-8\n# pylint: disable-msg=E1101,W0612\n\nimport pytest\n\nimport numpy as np\nimport pandas as pd\n\nfrom pandas import (Series, Timestamp)\n\nfrom pandas.compat import lrange\nfrom pandas.util.testing import (assert_series_equal)\n\n\ndef test_loc_getitem(test_data):\n inds = test_data.series.index... | [
[
"pandas.Timestamp",
"pandas.util.testing.assert_series_equal",
"numpy.random.randn",
"pandas.date_range",
"numpy.array",
"pandas.compat.lrange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
}
] |
SymmetricChaos/FiniteFields | [
"65258e06b7f04ce15223c1bc0c2384ef5e9cec1a"
] | [
"Visualization/FourierSquareWaveVisualization.py"
] | [
"from Fourier import Fourier, evaluate_series\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nx = np.linspace(-5,5,150)\npi = np.pi\nS = Fourier([0],[0],[0])\nfor i in [1,3,5]:\n A = Fourier([4/(pi*i)],[i])\n plt.plot(x,evaluate_series(A,x),color='gray',linestyle=\":\")\n S += A... | [
[
"matplotlib.pyplot.plot",
"numpy.linspace",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sergio1221/flask-backend | [
"11a9e0db5b5e664fcc820919d97039738176ac62"
] | [
"apps/fithm-service/libs/database/trade.py"
] | [
"from flask import current_app\r\nfrom libs.database import db_session\r\nfrom .portfolios import get_portfolios\r\nfrom apps.models import (\r\n Trade, Business, Portfolio, Pending, \r\n AccountPosition, Price, Account, Model,\r\n TradeRequest\r\n)\r\nimport pandas as pd\r\nfrom iexfinance.stocks import S... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"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": [],
"tensorflow": []
}
] |
helloworld1973/LivingNature_AIMakeUp | [
"06eefd202efc70d67e75994843ae0fdd6cd1e9ef"
] | [
"Ui_MakupGUI.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Form implementation generated from reading ui file 'C:\\pyprojects\\AIMakeup\\MakupGUI.ui'\n#\n# Created by: PyQt5 UI code generator 5.9\n#\n# WARNING! All changes made in this file will be lost!\n\nimport sys,os\nimport numpy as np\nimport cv2\nfrom PyQt5 import QtCore, QtWidgets\nfro... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
murphygroup/CellSegmentationEvaluator | [
"6f5d850148d52dc2d08576da42f700c4669d71e4"
] | [
"pipeline/segmentation/methods/convert_to_indexed_image.py"
] | [
"from skimage.io import imread\nimport sys\nimport numpy as np\nfrom os.path import join\nfrom skimage.color import rgb2gray\nimport bz2\nimport pickle\n# from skimage.measure import label\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import label\n\ndef convert_to_indexed(img):\n\tunique_cell = np.unique(im... | [
[
"scipy.ndimage.label",
"numpy.where",
"numpy.unique"
]
] | [
{
"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"... |
Ceglowa/shap | [
"585573ff8f93c39073f58948a886d78a7154a2f6"
] | [
"shap/plots/_waterfall.py"
] | [
"import numpy as np\nimport warnings\ntry:\n import matplotlib.pyplot as pl\n import matplotlib\nexcept ImportError:\n warnings.warn(\"matplotlib could not be loaded!\")\n pass\nfrom ._labels import labels\nfrom ..utils import safe_isinstance, format_value\nfrom . import colors\n\n\n# TODO: If we make a... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.axvline",
"matplotlib.pyplot.axhline",
"numpy.abs",
"numpy.min",
"numpy.arange",
"matplotlib.transforms.ScaledTranslation",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ioff",
"numpy.max",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bardkw/SMQTK-Descriptors | [
"21b449a64f4e19588c5e10e8ffe63473c7891610"
] | [
"smqtk_descriptors/impls/descriptor_element/solr.py"
] | [
"import time\nfrom typing import Any, Dict, Hashable, Mapping, Optional\n\nimport numpy\n\nfrom smqtk_descriptors import DescriptorElement\n\n\n# Try to import required module\ntry:\n import solr # type: ignore\nexcept ImportError:\n solr = None\n\n\nclass SolrDescriptorElement (DescriptorElement): # lgtm [... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shreeju/TensorNetwork | [
"00065ffdbbe0d6e4075045888e469e78e06dcf55"
] | [
"tensornetwork/backends/pytorch/pytorch_backend_test.py"
] | [
"import numpy as np\nfrom tensornetwork.backends.pytorch import pytorch_backend\nimport torch\nimport pytest\nfrom unittest.mock import Mock\n\ntorch_dtypes = [torch.float32, torch.float64, torch.int32]\ntorch_eye_dtypes = [torch.float32, torch.float64, torch.int32, torch.int64]\ntorch_randn_dtypes = [torch.float32... | [
[
"numpy.diag",
"torch.ge",
"torch.zeros",
"torch.sum",
"torch.le",
"torch.allclose",
"torch.ones",
"numpy.reshape",
"numpy.eye",
"numpy.matmul",
"torch.eye",
"numpy.zeros",
"torch.tensordot",
"torch.empty",
"numpy.linalg.eigh",
"numpy.random.rand",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yidazhao/fashioniq2020_retrieval | [
"337cd1d2f603e1e39cc95f886e290b0285011d9f"
] | [
"torch_functions.py"
] | [
"\n# TODO(lujiang): put it into the third-party\n# MIT License\n\n# Copyright (c) 2018 Nam Vo\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without... | [
[
"torch.norm",
"torch.transpose",
"torch.mm",
"torch.from_numpy",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.FloatTensor",
"torch.sort",
"torch.arange",
"torch.clamp",
"numpy.exp",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dhanani94/object-detection | [
"513dd38aefc19e3fde259b55ad83b12a6033ac54"
] | [
"src/detectors/ssd_detection.py"
] | [
"import cv2\nimport numpy as np\nimport pandas as pd\n\nfrom src.detectors.base_detector import BaseDetector\nfrom src.utils import timeit, read_json\n\n\nclass Detector(BaseDetector):\n \"\"\"Class ssd\"\"\"\n\n @timeit\n def __init__(self):\n super().__init__()\n self.swaprb = True\n ... | [
[
"numpy.random.uniform",
"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": []
}
] |
rdarie/electropy | [
"4357267806833d5d6749db497454aee3c3fd56b5"
] | [
"electropy/volume.py"
] | [
"import numpy as np\nfrom tqdm import tqdm\n\ndef potential(\n charge_objs,\n x_range=[-10, 10],\n y_range=[-10, 10],\n z_range=[-10, 10],\n h=0.01,\n ):\n \"\"\"Calculate potential in a volume\n\n Args:\n charge_objs: list of Charge objects\n x_range, y_range, z_range: [min, m... | [
[
"numpy.inner",
"numpy.gradient",
"numpy.rint",
"numpy.nan_to_num",
"numpy.ndenumerate",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tudorcebere/PyTorchXAI | [
"9ea70c1a1ab66323aa21484a8066512c9cd4fc43"
] | [
"examples/tutorial_01_tensorboard_mnist/mnist/model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nuse_cuda = torch.cuda.is_available()\ndevice = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(1, 20, 5, 1)\n self.... | [
[
"torch.nn.functional.log_softmax",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.cuda.is_available",
"torch.device",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
plumdeq/hypothtest | [
"f98ddc5c71873e5a9c63bbb99ba41a5ffcdc66c5"
] | [
"hypotest/stats/generalization_stats.py"
] | [
"# coding: utf8\n\"\"\"\nauthor: Asan Agibetov\n\n Copyright 2015-2017 Asan Agibetov <asan.agibetov@gmail.com>\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://w... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"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": [],
"tensorflow": []
}
] |
NuTufts/detr | [
"2c09de90cf1dbdb7aea3311c9c04a90a40b038f8"
] | [
"engine.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nTrain and eval functions used in main.py\n\"\"\"\nimport math\nimport os\nimport sys\nfrom typing import Iterable\n\nimport torch\n\nimport util.misc as utils\nfrom datasets.coco_eval import CocoEvaluator\nfrom datasets.panoptic_eval i... | [
[
"torch.stack",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pauljxtan/pystuff | [
"5abceaa95f8e39e6ef9ba9e937a745f4dc3996c4"
] | [
"pycompvis/compvis/tests.py"
] | [
"import numpy as np\nimport random\nimport unittest\n\nimport compvis.imgform.transforms as tr\n\nclass TestTransforms(unittest.TestCase):\n\n def test_2d_transforms_random_values(self):\n # Get some random vectors and parameters\n x = np.random.random(2)\n t = np.random.random(2)\n ... | [
[
"numpy.random.random",
"numpy.abs",
"numpy.cos",
"numpy.sin",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ioannis-krmp/Vitis-Tutorials | [
"c1b42f316ebe195b5f467dd3f949c003ba919647",
"c1b42f316ebe195b5f467dd3f949c003ba919647"
] | [
"Machine_Learning/Design_Tutorials/04-Keras_GoogleNet_ResNet/files/code/cifar10_eval_graph.py",
"Machine_Learning/Feature_Tutorials/tf2_quant_fine_tune/files/make_target.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n'''\n## © Copyright (C) 2016-2020 Xilinx, Inc\n##\n## Licensed under the Apache License, Version 2.0 (the \"License\"). You may\n## not use this file except in compliance with the License. A copy of the\n## License is located at\n##\n## http://www.apache.org/li... | [
[
"tensorflow.compat.v1.get_default_graph",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.Graph",
"tensorflow.compat.v1.import_graph_def",
"numpy.reshape",
"tensorflow.io.gfile.GFile",
"tensorflow.cast",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.pla... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
thmegy/mmdetection | [
"9d6420b9d2dd339d0952d2e1d8ec2dd22173226c"
] | [
"tools/train.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport copy\nimport os\nimport os.path as osp\nimport time\nimport warnings\n\nimport mmcv\nimport torch\nimport torch.distributed as dist\nfrom mmcv import Config, DictAction\nfrom mmcv.runner import get_dist_info, init_dist\nfrom mmcv.utils import... | [
[
"torch.distributed.get_rank"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IdeasLabUT/EDA-Artifact-Detection | [
"7c4bd467bec9099669ddf3581b00a9a843a2f136"
] | [
"MLP_inSample_AWW.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 23 12:03:59 2017\n\n@author: Kevin\n\"\"\"\n\nimport numpy as np\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import LeaveOneGroupOut,GridSearchCV\n\ndataPath = 'AlanWalksWales/'\n# Com... | [
[
"sklearn.neural_network.MLPClassifier",
"sklearn.metrics.roc_auc_score",
"numpy.arange",
"numpy.shape",
"sklearn.model_selection.LeaveOneGroupOut",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aditya-xq/Text-Emotion-Detection-Using-NLP | [
"00b1143e7921f59c93ddc945b53f11b70461a33d"
] | [
"main.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom nltk.corpus import stopwords\nfrom textblob import Word\nimport re\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVe... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.preprocessing.La... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
SBU-BMI/seer_distro_test | [
"2f41510a52d56587c12e26ec3a31b05b80a2fc3b"
] | [
"myscript.py"
] | [
"# Compute patch-level nuclear feature results.\n# Tumor-region only.\n# Change coll_name on line 830 to write to quip_comp.[your_collection_name]\nimport argparse\nimport json\nimport os\nimport subprocess\nimport sys\nimport time\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport cv2\nimport numpy... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.min",
"numpy.percentile",
"pandas.DataFrame",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
magland/nwb-jupyter-widgets | [
"3fdabd866d1aa38dfda1eedeae1f87965f2b8bd8"
] | [
"nwbwidgets/ophys.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom pynwb.ophys import RoiResponseSeries, DfOverF, PlaneSegmentation, TwoPhotonSeries\nfrom pynwb.base import NWBDataInterface\nfrom ndx_grayscalevolume import GrayscaleVolume\nfrom collections import OrderedDict\nfrom .utils.cmaps import linear_transfer_functi... | [
[
"matplotlib.colors.to_rgb",
"numpy.isfinite",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.nanstd",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MikkelMathiasen23/Variational_proteins | [
"e71bb7c900d9efe63521a622109349202cc7bfb7"
] | [
"train_models.py"
] | [
"import torch \r\nimport numpy as np\r\nfrom misc import data, c\r\nfrom models import *\r\n\r\nfrom torch import optim\r\nfrom scipy.stats import spearmanr\r\nfrom torch.distributions.normal import Normal\r\n\r\n\r\nclass training(torch.nn.Module):\r\n def __init__(self, **kwargs):\r\n super(training, self... | [
[
"numpy.abs",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"torch.arange",
"scipy.stats.spearmanr"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
JiechengZhao/shap | [
"d2042bab95bdd9c14f4bf7f1a8340bb6a2cf8733"
] | [
"tests/test_gradient.py"
] | [
"import matplotlib\nimport numpy as np\nmatplotlib.use('Agg')\nimport shap\n\ndef test_tf_keras_mnist_cnn():\n \"\"\" This is the basic mnist cnn example from keras.\n \"\"\"\n\n try:\n import tensorflow as tf\n from tensorflow.python import keras\n from tensorflow.python.keras.models ... | [
[
"tensorflow.python.keras.layers.Activation",
"tensorflow.python.keras.backend.image_data_format",
"tensorflow.python.keras.layers.MaxPooling2D",
"numpy.random.seed",
"tensorflow.python.keras.utils.to_categorical",
"numpy.random.choice",
"matplotlib.use",
"tensorflow.keras.backend.g... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"2.3",
"1.5",
"1.7",
"2.2"
]
}
] |
laravomfell/tvd_loss | [
"b30a925f95985a03ff70bfa40a6ec3662432779d"
] | [
"figure_1.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 14 09:34:38 2020\n\n@author: Lara Vomfell\n\nThis code generates Figure 1 in our code by generating some Poisson outcomes,\ncontaminating 10% of the data with outliers and then running a KLD-minimizing\nand a TVD-minimizing model and plott... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.random.seed",
"matplotlib.pyplot.savefig",
"numpy.ones",
"numpy.random.poisson",
"matplotlib.pyplot.ylabel",
"scipy.stats.poisson.pmf",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xdze2/graph_XRD | [
"3e6d9fd5f9ce35d117097a4edf45476a59356af6"
] | [
"sin2_psi/sin2_psi.py"
] | [
"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.3.3\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name... | [
[
"matplotlib.pylab.tight_layout",
"matplotlib.pylab.rcParams.update",
"numpy.polyfit",
"numpy.unique",
"numpy.asarray",
"numpy.isnan",
"matplotlib.pylab.title",
"numpy.genfromtxt",
"numpy.sin",
"numpy.ones",
"matplotlib.pylab.figure",
"matplotlib.pylab.ylabel",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
face-alignment-group-of-ahucs/SHN-based-2D-face-alignment | [
"fa7ec5f0a97a943858e851453c0b26f257b2f3c6"
] | [
"model.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 14 18:44:00 2018\n\n@author: xiang\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndef conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):\n \"3x3 convolution with padding\"\n return nn.C... | [
[
"torch.nn.ConvTranspose2d",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kimame04/text-subjectivity-conversion | [
"959eb8ab4f75706cf4a7f83de2ed3c784f557f1a"
] | [
"src/CNNModels.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass CnnTextClassifier(nn.Module):\n def __init__(self, vocab_size,emb_size,num_filters,num_classes, window_sizes=(3, 4, 5)):\n super(CnnTextClassifier, self).__init__()\n\n self.convs = nn.ModuleList([\n nn.Conv... | [
[
"torch.nn.functional.softmax",
"torch.max",
"torch.cat",
"torch.nn.Conv2d",
"torch.unsqueeze",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IMLHF/Real-Time-Voice-Cloning | [
"ae4aa2aa1605168d2f04275e1a45f6de2d88f3f0"
] | [
"synthesizer/models/architecture_wrappers.py"
] | [
"\"\"\"A set of wrappers useful for tacotron 2 architecture\nAll notations and variable names were used in concordance with originial tensorflow implementation\n\"\"\"\nimport collections\nimport tensorflow as tf\nfrom synthesizer.models.attention import _compute_attention\nfrom tensorflow.contrib.rnn import RNNCel... | [
[
"tensorflow.python.ops.tensor_array_ops.TensorArray",
"tensorflow.python.framework.tensor_shape.TensorShape",
"tensorflow.concat",
"tensorflow.python.ops.check_ops.assert_equal",
"tensorflow.python.ops.array_ops.zeros",
"tensorflow.python.ops.array_ops.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
geblanco/mc-transformers | [
"369d58abc83a2f84e05382a5bc5cd1e99f402e82"
] | [
"mc_transformers/mc_transformers.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... | [
[
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.config.experimental.list_physical_devices",
"numpy.atleast_2d",
"numpy.max",
"numpy.argmax",
"numpy.array",
"numpy.exp",
"numpy.sum",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shyam-Prasad-Gupta/ML-AI-DL | [
"470ab94e7f76bce1ad14f0ccc2575bd617100d4a"
] | [
"Digit Recogniser/src/network.py"
] | [
"\"\"\"\nnetwork.py\n~~~~~~~~~~\n\nA module to implement the stochastic gradient descent learning\nalgorithm for a feedforward neural network. Gradients are calculated\nusing backpropagation. Note that I have focused on making the code\nsimple, easily readable, and easily modifiable. It is not optimized,\nand om... | [
[
"numpy.dot",
"numpy.exp",
"numpy.random.randn",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexdiem/nvdbapi-V3 | [
"18265ee6d02aed17d6199e5ed42fe731c9320a08"
] | [
"nvdbgeotricks.py"
] | [
"\"\"\"\nEn samling hjelpefunksjoner som bruker nvdbapiv3-funksjonene til å gjøre nyttige ting, f.eks. lagre geografiske datasett\n\nDisse hjelpefunksjonene forutsetter fungerende installasjon av geopandas, shapely og en del andre ting som må \ninstalleres separat. Noen av disse bibliotekene kunne historisk av og t... | [
[
"pandas.read_sql_query",
"pandas.DataFrame"
]
] | [
{
"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": [],
"tensorflow": []
}
] |
imyu37/ray-optics | [
"5a4836bb95920f10ff2c30bba6d48be895a88da1"
] | [
"src/rayoptics/oprops/doe.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright © 2019 Michael J. Hayford\n\"\"\" Module for diffractive/holographic optical elements\n\n Classes that implement diffractive optics capabilities must implement\n the function phase() for use by the ray trace engine.\n\n The :class:`~.Diffractive... | [
[
"numpy.dot",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sivaabhishek/HandwrittenDigitRecognition | [
"02aa667d8042d04ec8f94b0465e6ee59d5810e37"
] | [
"generateClassifier.py"
] | [
"# Import the modules\nimport joblib\nfrom sklearn import datasets\nfrom skimage.feature import hog\nfrom sklearn.svm import LinearSVC\nimport numpy as np\nfrom collections import Counter\n\n# Load the dataset\ndataset = datasets.fetch_openml(\"mnist_784\")\nprint('Dataset')\n\n# Extract the features and labels\nfe... | [
[
"numpy.array",
"sklearn.datasets.fetch_openml",
"sklearn.svm.LinearSVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samplise/katib | [
"08234c3eccd43b11484fd78c9352eafbdc9152d8"
] | [
"pkg/suggestion/v1alpha2/grid_service.py"
] | [
"from logging import getLogger, StreamHandler, INFO, DEBUG\nimport itertools\nimport grpc\nimport numpy as np\nfrom pkg.apis.manager.v1alpha2.python import api_pb2\nfrom pkg.apis.manager.v1alpha2.python import api_pb2_grpc\nfrom . import parsing_util\n\nclass GridService(api_pb2_grpc.SuggestionServicer):\n def _... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peachy88/RL_trade_ES_futures | [
"8b681f1ef672f812bf78b78bb18d07ccab6d0b67"
] | [
"ressup.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom ressuputils import *\nimport matplotlib.pyplot as plt\n\n\ndef normalize(arr):\n 'arr - numpy.array'\n mu = np.mean(arr)\n st = np.std(arr)\n return ((arr - mu) / st), mu, st\n\ndef denorm(arr, mu, st):\n return (arr * st) + mu\n\n\n# def slopeRes(xs, ys... | [
[
"matplotlib.pyplot.title",
"pandas.DataFrame",
"numpy.std",
"numpy.mean",
"matplotlib.pyplot.show"
]
] | [
{
"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": []
}
] |
gabefreedman/portfolio | [
"53a67524aba57b0e94df4b5f273c9d340e7730fe"
] | [
"portfolio.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Global imports\nimport pandas as pd\nimport fix_yahoo_finance as yf\n\nfrom update_gsheets import Table\n\n\ns_name = 'MSFT'\ns = yf.Ticker(s_name)\n\n\n# Dummy classes for Indices and Portfolios. Will fill in later\n\nclass Index:\n \n def __repr__(self):\n return 'Index ... | [
[
"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": []
}
] |
Xinrihui/DeepLearningApp | [
"8d86b88251ee8d37358c642b1ec4a341767bfd17",
"8d86b88251ee8d37358c642b1ec4a341767bfd17"
] | [
"MachineTranslation/lib/layers/embedding_layer_xrh.py",
"LearnTF(Keras)/tensorflow2_training_lower_API.py"
] | [
"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n# 适用于 tensorflow >= 2.0, keras 被直接集成到 tensorflow 的内部\n\nfrom tensorflow.keras.layers import Layer\n\nimport tensorflow as tf\n\nimport numpy as np\n\n\nclass SharedEmbedding(Layer):\n \"\"\"\n 共享权重的 Embedding\n\n Author: xrh\n Date: 2022-1-5\n\n ref:\n ... | [
[
"tensorflow.matmul",
"tensorflow.shape",
"tensorflow.reshape",
"tensorflow.random_normal_initializer",
"tensorflow.random.normal",
"tensorflow.nn.embedding_lookup",
"numpy.random.randint"
],
[
"tensorflow.keras.Input",
"numpy.reshape",
"tensorflow.keras.losses.SparseCat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
quickhdsdc/Point-Tracking-for-Displacement-Measurement-in-Railway-Applications | [
"119b0bbc5076aa94d2801c7bfb0701204f2ae066"
] | [
"models/PoseResNet.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (Bin.Xiao@microsoft.com)\n# ------------------------------------------------------------------------------\n\nfrom __future__ import absolute_import\... | [
[
"torch.nn.Sequential",
"torch.nn.ConvTranspose2d",
"torch.load",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.init.normal_",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DSIP-UPatras/sEMG-based-gesture-recognition-mgeorgoula | [
"a5f8f234aebad671b38296730f46f0b637fadc64"
] | [
"code/part_b/dbc_capgmyo/vgg19_capgmyo_partb1.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"Make prediction and compute confusion matrix for modified input data\"\"\"\r\n\"\"\"PART B1 : Zero electrode column of emg data\"\"\"\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\nimport random\r\n\r\nimport os\r\nos.environ['PYTHONHASHSEED'] = '0'\r\n\r\n# The be... | [
[
"tensorflow.get_default_graph",
"numpy.random.seed",
"sklearn.metrics.confusion_matrix",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"numpy.argmax",
"tensorflow.set_random_seed",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
cuauv/software | [
"5ad4d52d603f81a7f254f365d9b0fe636d03a260",
"5ad4d52d603f81a7f254f365d9b0fe636d03a260"
] | [
"control/pid.py",
"vision/modules/gate.py"
] | [
"import operator\nimport math\nimport time\n\nimport numpy as np\n\nfrom auv_math.quat import Quaternion\nfrom auv_python_helpers.angles import heading_sub_degrees\n\nfrom shm import kalman, dvl, desires, settings_control, \\\n control_internal_depth, control_internal_heading, \\\n con... | [
[
"numpy.array"
],
[
"numpy.sqrt",
"numpy.median",
"numpy.linalg.norm",
"numpy.percentile",
"numpy.arctan2",
"numpy.mean",
"numpy.float32",
"numpy.var",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caiosba/covid-19 | [
"2a0f43f5004e7e39bd982eaa36185859cd9db88f"
] | [
"covid/data/mortality.py"
] | [
"import pandas as pd\nimport numpy as np\n\nfrom .cia_factbook import age_distribution\nfrom .data import DATA_PATH\n\n\ndef covid_mortality():\n \"\"\"\n Return a dataframe with COVID-19 mortality data from Neil M Ferguson, et. al.\n \"\"\"\n path = DATA_PATH / \"covid-mortality-imperial-college.csv\"\... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
samuelduchesne/archetypal | [
"2a7af343cc8fc6ee6aa34d3d87fa56384f2b0633"
] | [
"archetypal/template/dhw.py"
] | [
"\"\"\"archetypal DomesticHotWaterSetting.\"\"\"\n\nimport collections\nfrom statistics import mean\n\nimport numpy as np\nfrom eppy import modeleditor\nfrom sigfig import round\nfrom validator_collection import validators\n\nfrom archetypal import settings\nfrom archetypal.template.schedule import UmiSchedule\nfro... | [
[
"numpy.arange",
"numpy.deg2rad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ccrafael/tf-faster-rcnn | [
"4f446a4c1ebefcf6d92b5e01d2b6396bcbbf1a8d"
] | [
"lib/model/train_val.py"
] | [
"# --------------------------------------------------------\n# Tensorflow Faster R-CNN\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Xinlei Chen and Zheqi He\n# --------------------------------------------------------\nfrom __future__ import absolute_import\nfrom __future__ import divisi... | [
[
"numpy.random.get_state",
"tensorflow.multiply",
"tensorflow.summary.FileWriter",
"numpy.allclose",
"tensorflow.Variable",
"tensorflow.global_variables",
"tensorflow.variables_initializer",
"tensorflow.assign",
"tensorflow.python.pywrap_tensorflow.NewCheckpointReader",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
opticspy/lightpipes-python | [
"dbd66e46ca8263a6e9bf7690e5f2b2551f93f4cb"
] | [
"LightPipes/core.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as _np\nfrom scipy.special import hermite, genlaguerre\nfrom scipy.interpolate import RectBivariateSpline\nfrom .misc import backward_compatible\n\nUSE_CV2 = False\n\nif USE_CV2:\n import cv2\n\nUSE_SKIMAGE = False\nif USE_SKIMAGE:\n from skimage.restoration import unw... | [
[
"numpy.imag",
"numpy.sqrt",
"numpy.abs",
"numpy.meshgrid",
"numpy.asarray",
"scipy.interpolate.RectBivariateSpline",
"numpy.arange",
"scipy.special.genlaguerre",
"numpy.cos",
"numpy.sin",
"scipy.special.hermite",
"numpy.real",
"numpy.random.rand",
"numpy.iss... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"0.18",
"1.2",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.