text stringlengths 0 27.1M | meta dict |
|---|---|
import os
import glob
import random
import numpy as np
import subprocess
import audiosegment
import inflect
from num2words import num2words
inflect_engine = inflect.engine()
PAD = '_'
EOS = '~'
PUNC = '!\'(),-.:;?`'
SPACE = ' '
SYMBOLS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
en_symbols = SYMBOLS + PAD... | {
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// __BEGIN_LICENSE__
// Copyright (c) 2009-2013, United States Government as represented by the
// Administrator of the National Aeronautics and Space Administration. All
// rights reserved.
//
// The NGT platform is licensed under the Apache License, Version 2.0 (the
// "License"); you may not use this file excep... | {
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# Import Libraries
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
def stack_models(df_prepared, df_target, model_1, model_2, model_3, model_4):
"""
Stack all th... | {
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[STATEMENT]
lemma secureTT_secure: "secureTT \<Longrightarrow> secure"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. secureTT \<Longrightarrow> secure
[PROOF STEP]
unfolding secureTT_def secure_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>tr vl vl1. validSystemTrace tr \<and> TT tr \<and> B vl vl1... | {
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#include <boost/simd/include/native.hpp>
#include <boost/simd/preprocessor/stack_buffer.hpp>
#include <boost/simd/include/functions/aligned_load.hpp>
using boost::simd::aligned_load;
using boost::simd::native;
int main()
{
typedef native<double,BOOST_SIMD_DEFAULT_EXTENSION> simd_t;
BOOST_SIMD_ALIGNED_STACK_BUFFER... | {
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import numpy as np
from nntoolbox.losses import PinballLoss
import torch
class TestPinball:
def test_pinball(self):
"""
Adopt from https://www.tensorflow.org/addons/api_docs/python/tfa/losses/PinballLoss
"""
target = torch.from_numpy(np.array([0., 0., 1., 1.]))
input = torc... | {
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import numpy as np
from sklearn.isotonic import IsotonicRegression
from .curve_fit import project_convex_lip
class _BaseShapeIV:
def predict(self, X):
inds = np.searchsorted(self.x_, X[:, 0])
lb_x = self.x_[np.clip(inds - 1... | {
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from ..diversity import compound_class
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
def compound_class_plot(formula_list,
mass_list = [],
method = 'MSCC',
**kwargs):
"""
Docstring for function PyKrev.compound_c... | {
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import os
import os.path
import copy
import hashlib
import errno
import numpy as np
from numpy.testing import assert_array_almost_equal
from parse_config import args
from data.noise import build_for_cifar100
def check_integrity(fpath, md5):
if not os.path.isfile(fpath):
return False
md5o... | {
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# Let's look at how the API is used by importing a simple pretrained imagenet
# model and testing it out on a picture of a dog. We start with our imports, and
# can import our ResNet50 model-getter and decode_predictions tool for
# retrieving labels
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy ... | {
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[STATEMENT]
lemma invpst_baldR: "invpst l \<Longrightarrow> invpst r \<Longrightarrow> invpst (baldR l a r)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>invpst l; invpst r\<rbrakk> \<Longrightarrow> invpst (baldR l a r)
[PROOF STEP]
by (cases "(l,a,r)" rule: baldR.cases) (auto simp: invpst_baliL) | {
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/*******************************************************************************
procmon, Copyright (c) 2014, The Regents of the University of California,
through Lawrence Berkeley National Laboratory (subject to receipt of any
required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questi... | {
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program main
use DataInputM
use Structural3DApplicationM
use StructuralStrategyM
use SolvingStrategyM
use GIDDataOutputM
implicit none
type(Structural3DApplicationDT) :: application
type(StructuralStrategyDT) :: strategy
type(SolvingStrategyDT) :: solvingStrategy
call initFEM3D(ap... | {
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# Copyright 2021 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, ... | {
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program tel
integer n,m,l
parameter(n=96,m=38,l=13)
integer i,j,k,irow,icol,iu,itel
write(6,*) 'give irow'
read(5,*) irow
write(6,*) 'give icol'
read(5,*) icol
do i=1,n
do j=1,m
do k=1,l
do iu=1,6
itel = 6*((k-1)*n*m+n*(j-1)+i-1) +iu
if (... | {
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"""
A basic parser for tped plink formated files to a more convenient HDF5 format.
"""
import time
import h5py
import scipy as sp
def parse_single_12tped_to_hdf5(in_file_prefix='/home/bv25/data/Ls154/Ls154_12',
out_file_prefix='/home/bv25/data/Ls154/Ls154_12',
impute_t... | {
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# ##############################################
# Implementation of a (k,n) threshold scheme #
################################################
import sys
import numpy as np
# Parameters that needs to be changed manually are marked with: <--
par_nbr = 1 #<-- participant number
n = 5 #<--
k = 3 #<--
polynom = ... | {
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import cv2
import numpy as np
import sys
import shutil
import os
class Crop():
def __init__(self, image_name):
self.image_name = image_name
#####Filled polygons
#lpnts : polygons being built
self.lpnts = np.empty((1,0,2), dtype=np.int32)
#rpnts : ready polygons
... | {
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import numpy as np
import time
numMin = []
numMax = []
numAvg = []
ts = []
tformat = "%d/%m/%Y"
print(time.strftime(tformat))
tformat = "%H:%M"
#tformat = "%H:%M:%S"
for i in range(5):
numMin.append(np.random.randint(1,high=3))
numMax.append(np.random.randint(10,high=15))
numAvg.append(np.rand... | {
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#!/usr/bin/env python
import os
import argparse
import subprocess
import numpy as np
import pandas as pd
from Bio import Seq
import pyranges as pr
from pathlib import Path
from collections import defaultdict
def main(fasta_file, codon_list, input_file, output, aa_length):
results_name = (
output
... | {
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/-
Copyright (c) 2021 Joseph Myers. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Joseph Myers
-/
import linear_algebra.ray
import linear_algebra.determinant
/-!
# Orientations of modules
This file defines orientations of modules.
## Main definitions
* `orientatio... | {
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[STATEMENT]
lemma in_conc_True[iff]:
"\<And>L R. fin (conc L R) (True#p) = False"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>L R. fin (RegExp2NAe.conc L R) (True # p) = False
[PROOF STEP]
by (simp add:conc_def) | {
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# Call Syntax
person = @shared_var Citizen(
name::String = "Amin",
number::Float64 = 20.0,
)
person2 = Citizen(name = "Not-Amin", number = 1)
@test person.name == person2.name
@test person2.number == person2.number
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REBOL [
Title: "Red compile error test script"
Author: "Peter W A Wood"
File: %compile-error-test.r
Rights: "Copyright (C) 2013-2015 Peter W A Wood. All rights reserved."
License: "BSD-3 - https://github.com/red/red/blob/origin/BSD-3-License.txt"
]
~~~start-file~~~ "Red compile errors"
--test-- "ce-1 iss... | {
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from bayesianpy.network import NetworkFactory
class Selector:
def __init__(self, target, continuous=[], discrete=[]):
self.target = target
self._continuous = list(continuous)
self._discrete = list(discrete)
self._index = -1
self._all_variables = self._continuous + self._disc... | {
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[STATEMENT]
lemma Subset_fresh_iff [simp]: "a \<sharp> t SUBS u \<longleftrightarrow> a \<sharp> t \<and> a \<sharp> u"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<sharp> t SUBS u = (a \<sharp> t \<and> a \<sharp> u)
[PROOF STEP]
apply (rule obtain_fresh [where x="(t, u)"])
[PROOF STATE]
proof (prove)
goal (1... | {
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#%% Import modules
import time
from get_data import get_data
import model as m
from train import train
from comp import comp
import numpy as np
from os import listdir
from sklearn.metrics import r2_score
import torch
import torch.optim as optim
import torch.nn as nn
#%%
tStart = time.time()
#%% Path
dpath = './Data_... | {
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import .QueueUnion: Range, Queues
import Printf: @printf
const EPS = 1e-10
const DEBUG = false
@deprecate(
clip_front(elements, pqs, i, slope, offset, t),
clip(elements, Ref(pqs, i), +slope, +offset - t, Val(true))
)
@deprecate(
clip_back(elements, pqs, i, slope, offset, t),
clip(elements, Ref(pqs,... | {
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from __future__ import absolute_import, division, print_function
"""
This is for 3D selection in Glue 3d scatter plot viewer.
"""
import numpy as np
from glue.core import Data
from glue.config import viewer_tool
from glue.viewers.common.tool import CheckableTool
from glue.core.command import ApplySubsetState
from... | {
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import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model
from tensorflow.keras.optimizers import Adam
import numpy as np
from matplotlib import pyplot as plt
import sys
import os
import warnings
import logging
# TODO: Specify the directory that contains the `pyco... | {
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!****h* ROBODoc/H5D (F03)
!
! NAME
! H5D_PROVISIONAL
!
! PURPOSE
! This file contains Fortran 90 and Fortran 2003 interfaces for H5D functions.
! It contains the same functions as H5Dff_F90.f90 but includes the
! Fortran 2003 functions and the interface listings. This file will be compiled
! instead of H5Dff_F90.f... | {
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#!python3 ./infer.py
from time import sleep
from picamera import PiCamera
import numpy as np
import tarfile
import tempfile
import os
import timeit
import tvm
from tvm.contrib import graph_runtime as runtime
from tvm.contrib.download import download_testdata
from scipy.special import softmax
# Download the and load l... | {
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# Copyright 2020 DeepMind Technologies Limited. 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 ... | {
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[STATEMENT]
lemma Cl_F: "Br_1 \<B> \<Longrightarrow> Br_3 \<B> \<Longrightarrow> \<forall>A. Cl(\<F> A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Br_1 \<B>; Br_3 \<B>\<rbrakk> \<Longrightarrow> \<forall>A w. \<C> (\<F> A) w = \<F> A w
[PROOF STEP]
by (metis CF_rel Cl_fr_def FB4 Fr_4_def eq_ext' join_d... | {
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import os
import csv
import numpy as np
import torch
import pandas as pd
import matplotlib.pyplot as plt
if __name__ == "__main__":
base_path = os.path.join('results', 'mnistgen_ent')
exp_names = [f"mnistgen_ent1", f"mnistgen_ent2"]
dfs = []
for exp_name in exp_names:
results_path = os.path.jo... | {
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import numpy as np
from rdkit.Chem import Mol
from rdkit.Chem import DataStructs
from rdkit.Chem import rdFingerprintGenerator
import rdkit.Chem.Descriptors as Desc
def _fingerprint_fn_bits(generator):
def _fp(mol: Mol):
fingerprint = generator.GetFingerprint(mol)
array = np.zeros((0,), dtype=np.i... | {
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//
// asio.hpp
// ~~~~~~~~
//
// Copyright (c) 2003-2017 Christopher M. Kohlhoff (chris at kohlhoff dot com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
// See www.boost.org/libs/asio for documentation.
/... | {
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#importing required modules
import pandas as pd
import numpy as np
#function to create or check required files
def create_file():
try:
exp = pd.read_csv('ent_expense.csv')
except FileNotFoundError:
exp = pd.DataFrame({'Purchase': np.NaN,'Electricity': np.NaN,'Telecom': np.NaN,'Rent': np.NaN,'In... | {
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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... | {
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import os
import sys
import json
import yaml
import datetime
import numpy as np
from ptranking.base.ranker import LTRFRAME_TYPE
from ptranking.data.data_utils import SPLIT_TYPE
from ptranking.ltr_adhoc.eval.ltr import LTREvaluator
from ptranking.ltr_adhoc.eval.parameter import ValidationTape
from ptranking.ltr_divers... | {
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import cv2
import numpy as np
import svm_train as st
#Get the biggest Controur
def getMaxContour(contours,minArea=200):
maxC=np.array([])
maxArea=minArea
for cnt in contours:
area=cv2.contourArea(cnt)
if(area>maxArea):
maxArea=area
maxC=cnt
return maxC
#Ge... | {
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2018 University of Groningen
#
# 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
#
# U... | {
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#!/usr/bin/env python
"""
Key '0' - To select areas of background
Key '1' - To select areas of cervix
Key '2' - To select areas of channel
Key 'l' - go to next image
Key 'k' - go to previous iamge
Key 'd' - inc thickness
Key 'a' - dec thickness
Key 'r' - To reset mask
Key 's' - To save the results
Key 'm' - move pro... | {
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def azureml_main(frame1):
import matplotlib
matplotlib.use('agg')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.graphics.boxplots as sm
Azure = True
## Create a series of bar plots for the various levels of the
## string co... | {
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[STATEMENT]
lemma nn_integral_C:
assumes "m \<le> m'" and f[measurable]: "f \<in> borel_measurable (PiM {0..<n+m} M)"
and nonneg: "\<And>x. x \<in> space (PiM {0..<n+m} M) \<Longrightarrow> 0 \<le> f x"
and x: "x \<in> space (PiM {0..<n} M)"
shows "(\<integral>\<^sup>+x. f x \<partial>C n m x) = (\<integral... | {
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# -*- coding: utf-8 -*-
"""
This example solves a plug-flow reactor problem of hydrogen-oxygen combustion.
The PFR is computed by two approaches: The simulation of a Lagrangian fluid
particle, and the simulation of a chain of reactors.
"""
import cantera as ct
import numpy as np
######################################... | {
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#!/usr/bin/env python
# generative model associated with compression approaches to give us insights
# into how to compress better
import numpy as np
import matplotlib.pyplot as plt
# from .datasets import viz
# ================================================================ main
def unif_nbits(N=200, B=16, M=8, ... | {
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\section{The one-pass distributed algorithm}
The essence of the distributed strategy is to achieve almost perfect
parallelism, by splitting the input matrix into several smaller
matrices called \emph{jobs}. \\
\[
A^{m \times n} =
\begin{bmatrix}
A_1^{m \times c_1} \mid A_2^{m \times c_2} \mid \cdots \mid A_k^{m \tim... | {
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# Copyright Contributors to the Tapqir project.
# SPDX-License-Identifier: Apache-2.0
import math
from collections import defaultdict
from functools import partial
from pathlib import Path
import numpy as np
import pandas as pd
import pyro
import pyro.distributions as dist
import torch
from pyro.ops.indexing import V... | {
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"""Colour related classes and functions."""
from collections import deque
import random
import numpy
def wrap_hue(value):
while value >= 360:
value -= 360
while value < 0:
value += 360
return value
def rainbow(gap):
def hue_iterator():
hue = wrap_hue(random.randint(0, 359) ... | {
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"""
Class Features
Name: lib_data_io_ascii
Author(s): Francesco Avanzi (francesco.avanzi@cimafoundation.org), Fabio Delogu (fabio.delogu@cimafoundation.org)
Date: '20210603'
Version: '1.0.0'
"""
#######################################################################################
# Libra... | {
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[STATEMENT]
lemma finite_ImageI:
assumes "finite A"
assumes "\<And>a. a\<in>A \<Longrightarrow> finite (R``{a})"
shows "finite (R``A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite (R `` A)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. finite (R `` A)
[PROOF STEP]
note [[simp... | {
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###############################################################################
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
###############################################################################
imp... | {
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# Copyright (c) 2020, Huawei Technologies.All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law... | {
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import logging
from absl import app
from absl import flags
import numpy as np
import torch
from bgrl import *
log = logging.getLogger(__name__)
FLAGS = flags.FLAGS
# Dataset.
flags.DEFINE_enum('dataset', 'coauthor-cs',
['amazon-computers', 'amazon-photos', 'coauthor-cs', 'coauthor-physics', 'wiki-c... | {
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SUBROUTINE DIAGN(N,A,D,V,EPS)
* Diagonalization of a real symmetric NxN matrix
* Using the Jacobi method (from Numerical Recipes)
IMPLICIT NONE
INTEGER N,I,J,IP,IQ,NMAX
PARAMETER(NMAX=500)
DOUBLE PRECISION A(N,N),D(N),V(N,N),B(N),Z(N)
DOUBLE PRECISION EPS,SM,THR,G,H,C,S,T,THE... | {
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# confusion matrix is set up correctly
test_that("Included and own confusion matrix give identical results", {
data(iris)
tmp <- ranger::ranger(Species ~., data = iris)
confusion_matrix <- table(true = iris$Species,
predicted = tmp$predictions)
expect_equal(confusion_matrix, tmp$confusion.matrix)
})
# r... | {
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import os
import numpy as np
import pandas as pd
import pg8000
from sqlalchemy import create_engine
import datetime
import scipy.optimize
import scipy.interpolate
# templatenumeric = np.zeros((10, 1)) * np.nan
# templatebool = np.zeros((10, 1), dtype=bool)
engine = create_engine('postgresql+pg8000://user:password@vm... | {
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!-------------------------------------------------------------------------------
! Copyright (c) 2021, Whitman T. Dailey
! All rights reserved.
!
! Redistribution and use in source and binary forms, with or without
! modification, are permitted provided that the following conditions are met:
! 1. Redistributions of sou... | {
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"""MPI-INF-3DHP dataset."""
import copy
import json
import os
import pickle as pk
import numpy as np
import scipy.misc
import torch.utils.data as data
from hybrik.utils.bbox import bbox_clip_xyxy, bbox_xywh_to_xyxy
from hybrik.utils.pose_utils import cam2pixel_matrix, pixel2cam_matrix, reconstruction_error
from hybri... | {
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# coding: utf-8
# # Assignment 3: Recommendation systems
#
# Here we'll implement a content-based recommendation algorithm.
# It will use the list of genres for a movie as the content.
# The data come from the MovieLens project: http://grouplens.org/datasets/movielens/
# Note that I have not provided many doctests fo... | {
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# Copyright (c) 2009-2022 The Regents of the University of Michigan.
# Part of HOOMD-blue, released under the BSD 3-Clause License.
"""Angle potentials."""
from hoomd.md import _md
from hoomd.md.force import Force
from hoomd.data.typeparam import TypeParameter
from hoomd.data.parameterdicts import TypeParameterDict
i... | {
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# -*- coding: utf-8 -*-
"""example_depletion
A case that shows how the depletion is carried out.
Created on Mon Oct 11 21:30:00 2021 @author: Dan Kotlyar
Last updated on Mon Oct 11 21:45:00 2021 @author: Dan Kotlyar
"""
import numpy as np
from pyIsoDep.functions.maindepletionsolver import MainDepletion
from pyIsoD... | {
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!===================================================================2
! LANCZOS routines for BIGSTICK
!
! versions for 'new' parallelization scheme -- FALL 2011
!
! This code uses LAPACK ROUTINES
!
! LAPACK copyright statements and license
!
!Copyright (c) 1992-2013 The University of Tennessee and The University
! ... | {
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import datetime
import pandas_datareader.data as web
import pandas as pd
import numpy as np
from collections import defaultdict
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_score
import warnings
warnings.filt... | {
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/*!@file
* @copyright This code is licensed under the 3-clause BSD license.
* Copyright ETH Zurich, Laboratory of Physical Chemistry, Reiher Group.
* See LICENSE.txt for details.
*/
#define BOOST_FILESYSTEM_NO_DEPRECATED
#include "boost/filesystem.hpp"
#include "boost/program_options.hpp"
#include "Molassemb... | {
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from PIL import Image
from OpenGL.GL import *
import numpy as np
class Number:
_digit_textures = [None] * 10
_digit_to_path = [
'./assets/digit_0.png',
'./assets/digit_1.png',
'./assets/digit_2.png',
'./assets/digit_3.png',
'./assets/digit_4.png',
'./assets/digit... | {
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from __builtin__ import sorted
from docopt import docopt
import numpy as np
from representations.representation_factory import create_representation
def main():
args = docopt("""
Usage:
analogy_eval.py [options] <representation> <representation_path> <task_path>
Options:
... | {
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------------------------------------------------------------------------
-- Brandt and Henglein's subterm relation
------------------------------------------------------------------------
module RecursiveTypes.Subterm where
open import Algebra
open import Data.Fin using (Fin; zero; suc; lift)
open import Data.Nat
ope... | {
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#
# Copyright (c) 2020. Asutosh Nayak (nayak.asutosh@ymail.com)
#
# 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
#
import os
import re
from ... | {
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import logging
import os
import numpy as np
import tensorflow as tf
from cleverhans.attacks import CarliniWagnerL2
from cleverhans.compat import flags
from cleverhans.dataset import MNIST
from cleverhans.loss import CrossEntropy
from cleverhans.utils import grid_visual, AccuracyReport
from cleverhans.utils import set_... | {
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# coding: utf-8
# In[177]:
import os
import pandas as pd
get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import matplotlib.pyplot as plt
import hashlib
import sklearn as sk
import os
# In[178]:
path = '/home/catherinej/Downloads'
file = os.path.join(path, 'IrmaMudThicknessComparisons.xls... | {
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"""
Module of utility methods.
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import re
import os
import sys
import time
import pickle
import random
import scipy.sparse
import numpy as np
import pandas as pd
import xgboost as xgb
import lightgbm as lgb
import termcolor
import sklearn.metric... | {
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import wikipedia
from wordcloud import WordCloud, STOPWORDS
import os
from PIL import Image
import numpy as np
#currdir = os.path.dirname(__file__)
def get_wiki(query):
title = wikipedia.search(query)[0]
page = wikipedia.page(title)
return page.content
def create_wordcloud(text):
stopwords = set(STOPW... | {
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# -*- coding: utf-8 -*-
"""
Created on Tue Sep 3 15:30:41 2019
@author: autol
"""
#%%
from depends import ScaleX
from matrix_fun import Fill,Frob2,obj1,obj2,svdk,svd_,Prox,Frob1
import numpy as np
import time
from init_matrix import init_A1,init_A2,init_A3,init_A4
from sklearn.model_selection import ParameterGrid
fr... | {
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import numpy as np
from brl_gym.estimators.bayes_doors_estimator import BayesDoorsEstimator #, LearnableDoorsBF
from brl_gym.envs.mujoco.doors import DoorsEnv
from brl_gym.envs.mujoco.doors_slow import DoorsSlowEnv
from brl_gym.wrapper_envs.explicit_bayes_env import ExplicitBayesEnv
from brl_gym.wrapper_envs.env_sample... | {
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import os
import sys
#dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path = "/Users/neda/HiCPlus_pytorch/src"
import numpy as np
import argparse
import cooler
import matplotlib.pyplot as plt
import matplotlib.backends.backend_pdf
import torch
from torch.autograd import Variable
from scipy.stats.stats import... | {
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from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import math
import matplotlib as mpl
#-------------------------------------------------------------------------------
def strain_stress_divergence_hist():
# grid
fileGrid = Dataset("grid.40962.nc","r")
nVertices = len(fileGri... | {
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[STATEMENT]
lemma binomial_absorb_comp: "(n - k) * (n choose k) = n * ((n - 1) choose k)"
(is "?lhs = ?rhs")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (n - k) * (n choose k) = n * (n - 1 choose k)
[PROOF STEP]
proof (cases "n \<le> k")
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. n \<le> k \<Longrightar... | {
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import numpy as np
import matplotlib.pyplot as plt
# For drift
'''
for test in range(0,330):
# Removing anomalies.
if test not in [19,80,282,310]:
# Loading data.
data = np.load(f'D:/RLBot/ViliamVadocz/TestBot/data/test_{test:03}.npy')
# Selection position data.
pos = data[0]
... | {
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// Copyright (C) 2004-2008 The Trustees of Indiana University.
// Use, modification and distribution is subject to the Boost Software
// License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
// Authors: Douglas Gregor
// Andrew Lumsdaine
#include <... | {
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#include <iostream>
#include <vector>
#include <functional>
#include <cmath>
#include <Eigen/Dense>
#include "Derivative.h"
using Eigen::MatrixXd;
using Eigen::VectorXd;
using Eigen::Derivative;
using std::function;
using std::vector;
typedef function<double(VectorXd)> FuncDV;
typedef function<VectorXd(VectorXd)> ... | {
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[STATEMENT]
lemma reflexive:
fixes P :: pi
shows "P \<sim>\<^sup>s P"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P \<sim>\<^sup>s P
[PROOF STEP]
by(force simp add: substClosed_def intro: Strong_Early_Bisim.reflexive) | {
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import ctypes
import glob
import logging
import logging.config
import os
import shutil
from pathlib import Path
import numpy as np
import pandas as pd
from invoke import task
logging.config.fileConfig("logging.ini")
logger = logging.getLogger(__name__)
DEFAULT_SIM_DATADIR = os.getenv("SIM_DATADIR", "data")
DEFAULT_T... | {
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Require Import Kami.AllNotations.
Require Import StdLibKami.Fifo.Ifc.
Require Import StdLibKami.GenericFifo.Ifc.
Section Spec.
Context {ifcParams : Fifo.Ifc.Params}.
Class Params := {fifo : @Fifo.Ifc.Ifc ifcParams;
genericFifo : @GenericFifo.Ifc.Ifc (GenericFifo.Ifc.Build_Params
... | {
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import os, sys
import random
import itertools
import collections
import ast
import os.path as osp
import math
import multiprocessing
import numpy as np
class AttrDict(dict):
__getattr__ = dict.__getitem__
__setattr__ = dict.__setitem__
def deep_update(source, target):
for k, v in target.items():
... | {
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import cv2
import sys
import torch
import numpy as np
import pydicom
import os.path as osp
from copy import deepcopy
import torch.nn.functional as F
sys.path.insert(0, ".")
#from ct_iterator import C... | {
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#ifndef READ_PCAP_HPP
#define READ_PCAP_HPP
#define DETAIL_TIMING
#include <ParallelPcap/Pcap.hpp>
#include <ParallelPcap/Util.hpp>
#include <ParallelPcap/CountDictionary.hpp>
#include <boost/program_options.hpp>
#include <boost/archive/text_oarchive.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/file... | {
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import numpy as np
import matplotlib.pyplot as plt
# 42. Ten pregnant women were given an injection of pitocin to induce labor. Their
# systolic blood pressures immediately before and after the injection were:
before = [134, 122, 132, 130, 128, 140, 118, 127, 125, 142]
after = [140, 130, 135, 126, 134, 138, 124, 126... | {
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import numpy as np
import pandas as pd
import torch
import torch.nn.functional
import tqdm.auto as tqdm
from torch import nn
def _default_age_embedder(num_hidden, num_factors):
return nn.Sequential(
nn.utils.weight_norm(nn.Linear(1, num_hidden)),
nn.LeakyReLU(0.3),
nn.utils.weight_norm(nn.... | {
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/-
Copyright (c) 2018 Kenny Lau. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kenny Lau, Chris Hughes, Mario Carneiro
-/
import algebra.associated
import linear_algebra.basic
import order.zorn
import order.atoms
import order.compactly_generated
import tactic.abel
imp... | {
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!c***************************************************************
subroutine test1(accessor1,accessor2,width1,width2,test)
implicit none
!c PARAMETER STATEMENTS:
integer*8 accessor1,accessor2
integer width1,width2,i,j,k,test,eofFlag
complex*8, allocatable :: data1(:)
real*4,... | {
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"""
Bit Plane Slicing
"""
import cv2
import numpy as np
import matplotlib.pyplot as plt
def bit_plane_slicing(img_file, plane_level):
img = cv2.imread(img_file, 0)
plane_level = 1
transformed_img = np.zeros(shape=img.shape)
height, width = img.shape
for slice_factor in range(8):
for y in range(height):
... | {
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"""
Functions for converting between data formats
"""
from typing import Optional
import numpy as np
import pandas as pd
from .checks import (
is_flat_dataset,
is_sklearn_dataset,
is_stacked_dataset,
is_timeseries_dataset,
)
from .exceptions import TimekeepCheckError
def convert_timeseries_input(fun... | {
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\chapter{Undocumented Facilities}
Ns is often growing to include new protocols.
Unfortunately the documention doesn't grow quite as often.
This section lists what remains to be documented,
or what needs to be improved.
(The documentation is in the doc subdirectory of the ns source code
if you want to add to it. ... | {
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#
# Test does not work on some cards.
#
import threading
try:
from Queue import Queue # Python 2
except:
from queue import Queue # Python 3
import numpy as np
from numba import cuda
from numba.cuda.testing import unittest, CUDATestCase
def newthread(exception_queue):
try:
cuda.select_device(0)
... | {
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"""
This code is modified from Hengyuan Hu's repository.
https://github.com/hengyuan-hu/bottom-up-attention-vqa
"""
from __future__ import print_function
import _pickle as cPickle
import os
import json
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=FutureWarning)
impor... | {
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#encoding=utf8
from __future__ import print_function
import os
import six
import ast
import copy
import numpy as np
import paddle.fluid as fluid
class Placeholder(object):
def __init__(self):
self.shapes = []
self.dtypes = []
self.lod_levels = []
self.names = []
def __init... | {
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import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as optimize
import sys
from termcolor import colored
def line(x,a,x0) :
return a*x+x0
def texsci(number):
return "\\num{{{0:.2e}}}".format(number)
if __name__ == "__main__":
if(len(sys.argv) < 2) :
binfile = './data.csv'
... | {
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from typing import Dict
from rastervision.core.data.raster_transformer.raster_transformer \
import RasterTransformer
import numpy as np # noqa
class ReclassTransformer(RasterTransformer):
"""Reclassifies label raster
"""
def __init__(self, mapping: Dict[int, int]):
"""Construct a new Reclas... | {
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