arxiv_id stringlengths 0 16 | text stringlengths 10 1.65M |
|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 8 14:44:34 2018
@author: jack.lingheng.meng
"""
import tensorflow as tf
import numpy as np
import time
import gym
from Environment.LASEnv import LASEnv
from LASAgent.RandomLASAgent import RandomLASAgent
from LASAgent.LASAgent_Actor_Critic import... | |
import numpy as np
from scipy.spatial.distance import cdist
K = lambda x, y, bw: np.exp(-0.5*cdist(x, y, 'sqeuclidean') / bw**2)
def mmd(x: np.ndarray, y: np.ndarray, bw: float) -> float:
"""Computes the maximum mean discrepancy between two samples. This is a measure
of the similarity of two distributions th... | |
from os.path import dirname, abspath
import numpy as np
import cv2
import matplotlib.pyplot as plt
# Global constants and parameters
OUTPUT_DIR = dirname(dirname(abspath(__file__))) + "/output_images/"
LANE_REGION_HEIGHT = 0.63 # top boundary (% of ysize)
LANE_REGION_UPPER_WIDTH = 0.05 # upper width (% of ysiz... | |
import json
import argparse
import tensorflow.keras as keras
import numpy as np
import tensorflow as tf
from image_quality_assessment.utils import utils
import grpc
from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc
TFS_HOST = 'localhost'
TFS_PORT = 8500
def normalize_labels(labels):
la... | |
import math
from PIL import Image
import numpy as np
import filterdata as fd
import config
import scipy.misc
imagesbase = config.imagesbase
fullpath = config.fullpath
outputdir = config.outputdir
outputdir1 = config.outputdir if fullpath else ''
idx = 0
cnttxt = 0;
cntnon = 0;
phasenames = ['train', 'val']
for phase i... | |
from datetime import datetime, timedelta
from services.log_service import LogService
import torch
import numpy as np
from entities.metric import Metric
from entities.data_output_log import DataOutputLog
class LogServiceFake(LogService):
def __init__(self):
pass
def log_progress(
self,
... | |
## same as the analytic case but with the fft
import numpy as np
import matplotlib.pyplot as plt
from numpy.linalg import cond
import cmath;
from scipy import linalg as LA
from numpy.linalg import solve as bslash
import time
from convolution_matrices.convmat1D import *
from RCWA_1D_functions.grating_fft.gratin... | |
#import libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import seaborn
from matplotlib import pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.metrics... | |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import pathlib
import os
import zipfile
from multiprocessing import Pool
import datetime
from dateutil.relativedelta import relativedelta
def df_from_csv_with_geo(file_path, nrows=None):
"""Extract useful columns from ... | |
from dataclasses import dataclass
import gc
from torch import optim
import numpy as np
import random
import os
from src.models import *
# To eliminate randomness
def seed_everything(seed: int = 77):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(s... | |
from __future__ import print_function
import os
import glob # may cause segmentation fault in (C+Python) environment
import numpy as np
import cv2
import csv
import faiss
import pandas as pd
import utm
### For dataset
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from ... | |
from numpy.testing import assert_almost_equal, assert_raises
import numpy as np
from id3.data import load_data
import uuid
X = np.arange(20).reshape(10, 2)
y = np.arange(10).reshape(10, )
def test_load_data():
assert_raises(IOError, load_data.load_data, str(uuid.uuid4()))
X_, y_, _ = load_data.load_data("tes... | |
import numpy as np
import time
from datetime import timedelta
def sum_1to1000():
ret = 0
for i in range(1, 1001):
ret += i
return ret
def sum_1to1000_nparray():
a = np.ones(1000)
b = np.arange(1,1001)
return int(a.dot(b))
pass
if __name__ == "__main__":
start = time.time()
... | |
# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
import numpy as np
from svm_model import SVM_Model
from svm_model import SVM_Model
from sklearn.datasets import load_iris
... | |
import unittest
import shapely
import numpy as np
from mlx.od.nms import compute_nms, compute_iou
class TestNMS(unittest.TestCase):
def test_iou(self):
geom1 = shapely.geometry.box(0, 0, 4, 4)
geom2 = shapely.geometry.box(2, 2, 6, 6)
iou = compute_iou(geom1, geom2)
self.assertEqua... | |
from sympy import *
from tait_bryan_R_utils import *
x_t, y_t, z_t = symbols('x_t y_t z_t')
px, py, pz = symbols('px py pz')
om, fi, ka = symbols('om fi ka')
pxc, pyc, pzc = symbols('pxc pyc pzc')
omc, fic, kac = symbols('omc fic kac')
om_mirror = symbols('om_mirror')
ray_dir_x, ray_dir_y, ray_dir_z, ray_length = symb... | |
"""
Random choices functions
Copyright (c) 2019 Julien Kervizic
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, ... | |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
WIDTH = 12
HEIGHT = 3
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 14
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['font.family'] = 'Times New Roman'
SCHEDULELIST = [1,1,25]
ARMSLIST ... | |
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | |
import numpy as np
import torch
from perfectpitch.onsetsdetector.model import OnsetsDetector
from perfectpitch.utils.transcription import pianoroll_to_transcription
class Transcriber:
def __init__(self, onsets_detector_path, device):
self._device = torch.device(device)
self._onsets_detector = On... | |
import os
import numpy as np
import time
import sys
from PIL import Image
import cv2
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from DensenetModels import DenseNet121
from DensenetModels import DenseNet169
from DensenetModels... | |
# Copyright 2022 The DDSP Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
#////////////////////////////////////////////////////////////////
#//
#// Python modules
#//
#// -------------------------------------------------------------
#//
#// AUTHOR: Miguel Ramos Pernas
#// e-mail: miguel.ramos.pernas@cern.ch
#//
#// Last update: 04/10/2017
#//
#// -----------------------------------------... | |
"""
Primary function of recipe here
"""
import mbuild as mb
import numpy as np
from numpy import sqrt, pi, arctan2, arcsin
class build_silica_NP(mb.Compound):
"""
Build a tethered_NP compound.
Example would be a silica nanoparticle covered in alkane chains
Parameters
----------
Args:
n_c... | |
import argparse
import logging
import os, sys
import csv
import numpy as np
import random
import time
from run_ple_utils import make_ple_env
def main_event_dependent():
parser = argparse.ArgumentParser()
parser.add_argument('--test_env', help='testv environment ID', default='ContFlappyBird-v3')
parser.add... | |
"""Function to show an example of the created points of the sampler.
"""
import numpy as np
import matplotlib.pyplot as plt
def scatter(subspace, *samplers):
"""Shows (one batch) of used points in the training. If the sampler is
static, the shown points will be the points for the training. If not
the po... | |
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2017 by University of Kassel and Fraunhofer Institute for Wind Energy and
# Energy System Technology (IWES), Kassel. All rights reserved. Use of this source code is governed
# by a BSD-style license that can be found in the LICENSE file.
import numpy as np
import pytest
i... | |
from sklearn.ensemble import RandomForestRegressor
import time
from sklearn.base import BaseEstimator
from typing import Optional, Dict, Union, Tuple
import pandas as pd
import numpy as np
from sklearn.linear_model import RidgeCV
def train_ridge_lr_model(
xtrain: Union[np.ndarray, pd.DataFrame],
ytrain: Union... | |
import unittest
import numpy as np
import numpy.testing as npt
import wisdem.drivetrainse.layout as lay
npts = 12
ct = np.cos(np.deg2rad(5))
st = np.sin(np.deg2rad(5))
class TestDirectLayout(unittest.TestCase):
def setUp(self):
self.inputs = {}
self.outputs = {}
self.discrete_inputs = {}... | |
# -*- coding: utf-8 -*-
"""
Written by Daniel M. Aukes
Email: danaukes<at>gmail.com
Please see LICENSE for full license.
"""
import pynamics
from pynamics.frame import Frame
from pynamics.variable_types import Differentiable,Constant,Variable
from pynamics.system import System
from pynamics.body import Body
from pynam... | |
"""Provides data structures for encapsulating loss data."""
import numpy
class Loss:
"""Encapsulates training loss data.
.. py:attribute:: label
A string that will be used in graph legends for this loss data.
.. py:attribute:: loss_values
A numpy.ndarray containing the training loss dat... | |
"""
This file is the main source file of the ProcessMCRaT library which is used to read and process
the results of a MCRaT simulation
Written by: Tyler Parsotan April 2021
"""
import os
import astropy as ap
import h5py as h5
import numpy as np
from astropy import units as u
from astropy import constants as const
from... | |
from DataSocket import TCPSendSocket, RAW, TCPReceiveSocket
import time
import numpy as np
import threading
import sys
send_port = 4242
receive_port = 4343
ip = '0.0.0.0'
start_time = time.time()
# define function to print the echo back from matlab
def print_data(data):
global start_time
now = time.time()
... | |
import copy
import numpy as np
import torch
from sklearn.cluster import KMeans
from utils.checkings import *
from torch.optim.lr_scheduler import LambdaLR
os.environ["OMP_NUM_THREADS"] = "8" # Limit the CPU usage during KMeans clustering
def create_folders(names, data_dir):
datasets = ["ethucy", "SDD"]
folde... | |
"""
Copyright 2019 Zachary Phillips, Waller Lab, University of California, Berkeley
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of cond... | |
#!/usr/bin/env python3
# Copyright 2017 Christian Henning
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... | |
#!/usr/bin/env python3s
import os.path
import tensorflow as tf
import helper
import warnings
from distutils.version import LooseVersion
import project_tests as tests
from tqdm import tqdm
import numpy as np
KEEP_PROB = 0.8 #lower value will help generalize more (but with fewer epochs, higher keep_prob creates more cle... | |
'''
File:
get_historical.py
Authors:
Prakash Dhimal, Kevin Sanford
Description:
Python module to get historical prices and volumes for a given company
'''
import numpy as np
import normalize as scale
'''
@param - historical - list containing historical prices, and volumes
@retruns opening - list containing dail... | |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | |
import numpy as np
def test_generate_minimax_move():
"""
First checking whether the agent can return an action.
Then it asserts the agent will producing valid move.
Next, it will test if the agent can produce a winning move
given a board state
"""
from agents.agent_minimax import generate_... | |
import sys
sys.dont_write_bytecode = True
import numpy as np
import scipy.sparse as sp
from network_propagation_methods import sample_data, netprop, minprop_2, minprop_3
#### Parameters #############
# convergence threshold
eps = 1e-6
# maximum number of iterations
max_iter = 1000
# diffusion parameters
alphaP, alpha... | |
import numpy as np
import os
from datetime import datetime
from pytz import timezone
import matplotlib.pyplot as plt
from agent_qlean import QLearnAgent
from agent_bandit import BanditAgent
from environment import Environment
from simulator import parameters
from simulator.transaction_model import TransactionModel
from... | |
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PyTorchDisentanglement.utils.file_utils import Logger
class BaseModel(nn.Module):
def __init__(self):
super(BaseModel, self).__init__()
self.params_loaded = False
def setup(self, params, logge... | |
import os
import re
import itertools
import cv2
import time
import numpy as np
import torch
from torch.autograd import Variable
from utils.craft_utils import getDetBoxes, adjustResultCoordinates
from data import imgproc
from data.dataset import SynthTextDataSet
import math
import xml.etree.ElementTree as elemTree
#... | |
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x, y = np.random.rand(2, 100) * 4
hist, xedges, yedges = np.histogram2d(x, y, bins=4)
elements = (len(xedges) - 1) * (len(yedges) - 1)
xpos, ypos = np.meshgrid(xedge... | |
from jaxns.nested_sampling import NestedSampler
from jaxns.prior_transforms import PriorChain, MVNDiagPrior, UniformPrior, GaussianProcessKernelPrior, HalfLaplacePrior,MVNPrior
from jaxns.plotting import plot_cornerplot, plot_diagnostics
from jaxns.gaussian_process.kernels import RBF
from jax.scipy.linalg import solve_... | |
from tqdm import tqdm
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Union
import ipdb
import collections
import random
import torch
from copy import deepcopy
from torch.nn.utils.rnn import pad_sequence
from transformers.tokenization_utils_base import BatchEncoding
def _sa... | |
from cupy import _util
# expose cache handles to this module
from cupy.fft._cache import get_plan_cache # NOQA
from cupy.fft._cache import clear_plan_cache # NOQA
from cupy.fft._cache import get_plan_cache_size # NOQA
from cupy.fft._cache import set_plan_cache_size # NOQA
from cupy.fft._cache import get_plan_cache... | |
import numpy as np
import mdtraj as md
import pytest
from scattering.utils.io import get_fn
from scattering.utils.run import run_total_vhf, run_partial_vhf
@pytest.mark.parametrize("step", [1, 2])
def test_run_total_vhf(step):
trj = md.load(get_fn("spce.xtc"), top=get_fn("spce.gro"))
chunk_length = 4
n_... | |
import tensorflow as tf
from tensorflow import keras
print(tf.VERSION)
print(tf.keras.__version__)
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.inception_v3 import preprocess_input
import numpy as np
import argparse
import matplotlib.pyplot as plt
import json
parser = argparse.... | |
"""Utility functions module."""
import cvxpy as cp
import datetime
import logging
import numpy as np
import os
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
import psychrolib
import pvlib
import re
import scipy.sparse
import subprocess
import sys
import time
import typi... | |
"""Prepare CelebAHQ dataset"""
import os
import torch
import numpy as np
from PIL import Image
from .segbase import SegmentationDataset
class CelebaHQSegmentation(SegmentationDataset):
NUM_CLASS = 15
def __init__(self, root='/home/mo/datasets/face_mask/', split='train', mode=None, transform=None, **kwargs):... | |
"""
Test functions for the shoyu.py module
"""
import os
import pickle
import numpy as np
from ramannoodles import shoyu
# open spectra library
SHOYU_DATA_DICT = pickle.load(open('raman_spectra/shoyu_data_dict.p', 'rb'))
def test_download_cas():
"""
Test function that confirms that the raman_spectra/ directo... | |
import numpy as np
import pandas as pd
from pandas.io.parsers import read_csv
from BOAmodel import *
from collections import defaultdict
""" parameters """
# The following parameters are recommended to change depending on the size and complexity of the data
N = 2000 # number of rules to be used in SA_patternbase... | |
import sys
import os
sys.path.insert(
0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src"))
)
import torch
import torch.nn.functional as F
import numpy as np
import imageio
import util
import warnings
from data import get_split_dataset
from render import NeRFRenderer
from model import make_mode... | |
from config import TIMITConfig
from argparse import ArgumentParser
from multiprocessing import Pool
import os
from TIMIT.dataset import TIMITDataset
if TIMITConfig.training_type == 'H':
from TIMIT.lightning_model_h import LightningModel
else:
from TIMIT.lightning_model import LightningModel
from sklearn.met... | |
"""
Generate data for the diffusion forward model.
Author:
Panagiotis Tsilifis
Date:
6/12/2014
"""
import numpy as np
import fipy as fp
import os
import matplotlib.pyplot as plt
# Make the source
nx = 101
ny = nx
dx = 1./101
dy = dx
rho = 0.05
q0 = 1. / (np.pi * rho ** 2)
T = 0.3
mesh = fp.Grid2D(dx=dx... | |
"""
setup.py file for SWIG example
"""
from distutils.core import setup, Extension
import numpy
polyiou_module = Extension('_polyiou',
sources=['polyiou_wrap.cxx', 'polyiou.cpp'],
)
setup(name = 'polyiou',
version = '0.1',
author = "SWIG Docs",
... | |
# -*- coding: utf-8 -*-
# Copyright 1999-2018 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | |
"""
author: Junxian Ye
time: 12/22/2016
link: https://github.com/un-knight/coursera-machine-learning-algorithm
"""
import numpy as np
import pandas as pd
import sklearn.svm
import seaborn as sns
from matplotlib import pyplot as plt
from func import tools
def gaussian_kernel(x1, x2, sigma=1.0):
diff = x1 - x2
... | |
from __future__ import division
import math
import numpy as np
import unittest
from chainer import testing
from chainercv.utils import tile_images
@testing.parameterize(*testing.product({
'fill': [128, (104, 117, 123), np.random.uniform(255, size=(3, 1, 1))],
'pad': [0, 1, 2, 3]
}))
class TestTileImages(uni... | |
import numpy as np
import time
class mpc_controller():
def __init__(self,
env,
dyn_model,
horizon = 20,
cost_fn = None,
num_simulated_paths = 1000,):
self.env = env
self.dyn_model = dyn_model
self.horizon =... | |
# -*- coding: utf-8 -*-
# Author: Jiajun Ren <jiajunren0522@gmail.com>
import os
import numpy as np
import pytest
from renormalizer.spectra import SpectraOneWayPropZeroT, SpectraTwoWayPropZeroT, SpectraExact
from renormalizer.spectra.tests import cur_dir
from renormalizer.tests import parameter
from renormalizer.uti... | |
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import torch.nn.functional as F
import pdb
from mmd_comp import MultipleKernelMaximumMeanDiscrepancy, JointMultipleKernelMaximumMeanDiscrepancy
from kernels import GaussianKernel
def Entropy(input_):
bs = input_.s... | |
"""Unittests for the functions in svid_location, using the true data from 2020-02-11."""
import unittest
import numpy.testing as npt
import pandas.testing as pt
from itertools import product
import numpy as np
import math
from scipy.special import expit
from gnssmapper.algo.FPL import FourParamLogisticRegression
c... | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import json
SUMMARY_LOG_SAVE_PATH = ""
DENSENET_MODEL_PREDICT_RESULT_FILE = ""
RESNET_MODEL_PREDICT_RESULT_FILE = ""
XCEPTION_MODEL_PREDICT_RESULT_FILE = ""
def Var... | |
import numpy as np
import time
from .gdtwcpp import solve
from .signal import signal
from .utils import process_function
class GDTW:
def __init__(self):
# generic input vars
self.x = None
self.x_a = None
self.x_f = None
self.y ... | |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 14 14:34:24 2022
@author: Manuel Huber
"""
import os.path
import multiprocessing
from multiprocessing import Process, Manager
import ee
import geemap
import numpy as np
Map = geemap.Map()
import matplotlib.pyplot as plt
from colour import Color
#from ... | |
import sys
import os
import time
from json_tricks.np import dump, load
from functools import reduce
import numpy as np
import tensorflow as tf
from sklearn.linear_model import LinearRegression
# from scipy.sparse import hstack, csr_matrix, csr
import pandas as pd
import edward as ed
from edward.models import Normal
i... | |
# Image-based testing borrowed from vispy
"""
Procedure for unit-testing with images:
Run individual test scripts with the PYQTGRAPH_AUDIT environment variable set:
$ PYQTGRAPH_AUDIT=1 python pyqtgraph/graphicsItems/tests/test_PlotCurveItem.py
Any failing tests will display the test results, standard... | |
# -*- coding: utf-8 -*-
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | |
#!/usr/bin/env python
import random, math
import numpy as np
import game
from randomPlayer import RandomPlayer
import play
class OmniscientAdversary:
def __init__(self, nPlay):
self._rp = RandomPlayer()
self._rand = random.Random()
self._epsSame = 1e-6
self._nPlay = nPlay
def ... | |
# coding: utf-8
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License... | |
import os
import streamlit.components.v1 as components
import streamlit as st
import time
import numpy as np
import IPython.display as ipd
#ipd.Audio(audio, rate=16000)
from online_scd.model import SCDModel
from online_scd.streaming import StreamingDecoder
import timeit
import base64
import scipy.io.wavfile
from on... | |
'''
03_WindyGridWorld_nStepSARSA_OffPolicy.py : n-step off-policy SARSA applied to Windy Grid World problem (Example 6.5)
Cem Karaoguz, 2020
MIT License
'''
import numpy as np
import pylab as pl
from IRL.environments.Gridworlds import StochasticGridWorld
from IRL.agents.TemporalDifferenceLearning import nStepOffPoli... | |
from competition_and_mutation import Competition, MoranStyleComp, normal_fitness_dist, uniform_fitness_dist
from colourscales import get_colourscale_with_random_mutation_colour
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
def example1():
# Run a single simulation of algorithm 1
... | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
import unittest
import numpy as np
# pyre-fixme[21]: Could not find module `pytest`.
import pytest
# pyre-fixme[21]: Could not find `pyspark`.
from pyspark.sql.functions import asc
# pyre-fixme[21]: Could ... | |
from __future__ import division, print_function, absolute_import
import time
import numpy as np
import tensorflow as tf
from scipy.stats.mstats import gmean
from tefla.da import tta
from tefla.da.iterator import BatchIterator
from tefla.utils import util
class PredictSessionMixin(object):
def __init__(self, we... | |
import os
import logging
import queue
import re
import shutil
import string
import torch
import torch
import torch.nn as nn
import torch.nn.functional as F
import tqdm
import numpy as np
import ujson as json
from torch.utils.data import Dataset
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take ... | |
import numpy as np
import itertools as it
#solve
#A.T*Ax=A.T*b
#x=inv(A.T*A)*A.T*b
#Z=inv(H.T*H)*H.T*y
def min2_mtx(A,b):
x=np.matmul(A.T,A)
x=np.linalg.inv(x)
x=np.matmul(x,A.T)
x=np.matmul(x,b)
return x
#euler
#t time array
#y0 init value
#f function f(t,y)
def euler(t,y0,f):
h=t[1]-t[0]
... | |
# -*- coding: utf-8 -*-
"""
Created on Sat May 25 14:21:27 2019
@author: Tin
"""
import numpy as np
import pandas as pd
import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import warnings
... | |
#!python3
# ##----------------------------------------## #
# Author: M. Burak Yesilyurt #
# Truss Optimization by Employing #
# Genetic Algorithms #
# ##----------------------------------------## #
# Importing necessary modules
# To run the code below, imported... | |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | |
"""
populates Vivus() with all geometric quantities including
Diameter() which carries (x,y) coordinates of min/max diameter endpoints
and pixel length values
"""
import logging
import numpy as np
from itertools import product as iterp
from . import params as p
mlg = logging.getLogger(__name__)
def xys2dists(... | |
import logging
import os
import pickle
from collections import defaultdict
from typing import Dict
import h5py # type: ignore
import numpy as np
from probing_project.utils import Observation
from rich.progress import track
from torch.nn import CrossEntropyLoss
from .pos_task import POSTask
logger = logging.getLogge... | |
# -*- coding: utf-8 -*-
"""
shepherd.calibration
~~~~~
Provides CalibrationData class, defining the format of the SHEPHERD calibration
data
:copyright: (c) 2019 by Kai Geissdoerfer.
:license: MIT, see LICENSE for more details.
"""
import yaml
import struct
from scipy import stats
import numpy as np
from pathlib imp... | |
import numpy as np
import copy
from operator import itemgetter
# from sympy import expand
def rollout_policy_fn(board):
"""a coarse, fast version of policy_fn used in the rollout phase."""
# rollout randomly
action_probs = np.random.rand(len(board.availables))
return zip(board.availables, ac... | |
#!/usr/bin/env python3
#
# Converter from Keras saved NN to JSON
"""
____________________________________________________________________
Variable specification file
In additon to the standard Keras architecture and weights files, you
must provide a "variable specification" json file with the following
format:
{
... | |
import pandas as pd
import numpy as np
from sklearn import datasets
from Kmeans_python.fit import fit
# Test function for center
def test_edge():
test_df = pd.DataFrame({'X1': np.zeros(10), 'X2': np.ones(10)})
centers, labels = fit(test_df, 1)
print(labels)
assert centers.all() == np.array([0, 1]).... | |
"""
Analytics Vidhya Jobathon
File Description: Utils + Constants
Date: 27/02/2021
Author: vishwanath.prudhivi@gmail.com
"""
#import required libraries
import pandas as pd
import numpy as np
import logging
import xgboost as xgb
from catboost import CatBoostClassifier, Pool... | |
#############################################################################################################
################################################## IMPORTS ##################################################
####################################################################################################... | |
import copy
import time
import numpy as np
from ray.rllib.agents.pg import PGTrainer, PGTorchPolicy
from marltoolbox.envs.matrix_sequential_social_dilemma import IteratedPrisonersDilemma
from marltoolbox.examples.rllib_api.pg_ipd import get_rllib_config
from marltoolbox.utils import log, miscellaneous
from marltoolbo... | |
import os
from abc import ABC, abstractmethod
from pathlib import Path
from configobj import ConfigObj
from lmfit.models import LorentzianModel, QuadraticModel, LinearModel, ConstantModel, PolynomialModel
from matplotlib import pyplot as plt
from scipy.signal import savgol_filter
try:
from plot_python_vki import ... | |
"""
Data preparation for Pendigits data.
The result of this script is input for the workshop participants.
This dataset has only numerical data (16 columns), with little meaning (originating from
downsampling coordinates in time from digits written on a digital pad)
Done here:
- mapping of outliers: b'yes'/b'no' to 1... | |
import numpy as np
import matplotlib.pyplot as plt
import os, random
import json
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms, models
from collections import OrderedDict
from PIL import Image
import time
import ... | |
import abc
import logging
import pprint
import random
import typing
from operator import itemgetter
from numpy.random import RandomState
import d3m.exceptions as exceptions
from .template_hyperparams import Hyperparam
_logger = logging.getLogger(__name__)
DimensionName = typing.NewType('DimensionN... | |
# (C) William W. Cohen and Carnegie Mellon University, 2016
import theano
import theano.tensor as T
import theano.sparse as S
import theano.sparse.basic as B
from . import matrixdb
import numpy
def debugVar(v,depth=0,maxdepth=10):
if depth>maxdepth:
print('...')
else:
print('| '*(depth+1), end=' ')
pr... | |
# Copyright 2017-2020 Lawrence Livermore National Security, LLC and other
# Hatchet Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: MIT
import glob
import struct
import re
import os
import traceback
import numpy as np
import pandas as pd
import multiprocessing as mp
import... | |
#!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2016 CNRS
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation ... | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
# Monkey-patch because I trained with a newer version.
# This can be removed once PyTorch 0.4.x is out.
# See https://discuss.pytorch.org/t/question-about-rebuild-tensor-... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.