text stringlengths 0 27.1M | meta dict |
|---|---|
[STATEMENT]
lemma karatsuba_main_step: fixes f :: "'a :: comm_ring_1 poly"
assumes f: "f = monom_mult n f1 + f0" and g: "g = monom_mult n g1 + g0"
shows
"monom_mult (n + n) (f1 * g1) + (monom_mult n (f1 * g1 - (f1 - f0) * (g1 - g0) + f0 * g0) + f0 * g0) = f * g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
... | {
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"... |
[STATEMENT]
lemma cas_subset :
assumes "ces v1 es v2 subs1"
assumes "subs1 \<subseteq> subs2"
shows "ces v1 es v2 subs2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. SubRel.ces v1 es v2 subs2
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
SubRel.ces v1 es v2 subs1
subs1 \<subseteq> subs2... | {
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# -*- coding: utf-8 -*-
"""
Created: 08/11/2018
Last update: 12/11/2018
Alex Daniel, Sir Peter Mansfield Imaging Centre, The University of Nottingham, 2018.
pton (p-to-n or par-to-nifti) converts Philips PAR/REC format data into compressed nifti data. This tool is designed as
an alternative to the popular ptoa.exe whi... | {
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from flask import Flask, jsonify, request, redirect, make_response
import logging, sys, time
import cv2
import numpy as np
import os, re
from werkzeug.datastructures import ImmutableMultiDict
import face_recognition
import json
from google.cloud import firestore
from datetime import date
# Intializion code. Also reduc... | {
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"include": true,
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import gcsfs
import pandas as pd
import numpy as np
import statistics as stats
from line_reader import LineReader
from table_identifier import TableIdentifier
from table_fitter import TableFitter
from pdf_annotator import PDFAnnotator
from doc_ai_parser import DocParser
from table_to_df import Table2Df
fs = gcsfs.GCS... | {
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import os
import random
import numpy as np
import tensorflow as tf
from tensorflow import keras
from PIL import Image
from train_data import TrainData
class ModelLoss():
def __init__(self):
pass
def call(self, y_true, y_pred):
return tf.losses.mean_squared_error(y_true, y_pred)
def buil... | {
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import codecs
import pandas as pd
import numpy as np
import mysql.connector as MySQL
import re
arabic_diacritics = re.compile("""
ّ | # Tashdid
َ | # Fatha
ً | # Tanwin Fath
ُ | # Damma
... | {
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!##############################################################################
!# ****************************************************************************
!# <name> timescalehierarchy </name>
!# ****************************************************************************
!#
!# <purpose>
!# This module maintains ti... | {
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import os
import shutil
import argparse
import time as t
import torch
import numpy as np
import torch.nn as nn
import tensorboardX as tX
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T
from torch.utils.data import DataLoader
from dataloader.KITTI2015_loader i... | {
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def maketree(n=12, gamma=0.1, algorithm='kurtosis-matching', k=10,
tol=1e-12, extra_precision=False):
'''
Generate and plot the willow tree in a single step.
Input
---------------------------------------------------------------------------
n: int, optional argument. The number of space... | {
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from __future__ import absolute_import
import sys
sys.path.append('./')
import argparse
import os
import os.path as osp
import numpy as np
import math
import time
from PIL import Image, ImageFile
import torch
from torch import nn, optim
from torch.backends import cudnn
from torch.utils.data import DataLoader
from tor... | {
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import torch
import torchvision
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.utils import save_image
from datasets import get_datase... | {
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import gym
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pickle
import trfl
from DQN_utilities import *
# env parameters
STATE_SIZE_0 = 52
ACTION_SIZE_0 = 5
STATE_SIZE_1 = 36
ACTION_SIZE_1 = 11
RESTORE_PAR = False
ckpt_dir = "./checkpoints_2/dataCache.ckpt-4999"
# define network hy... | {
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import caffe
import numpy as np
np.set_printoptions(threshold='nan')
MODEL_FILE = 'train_val.prototxt'
PRETRAIN_FILE = 'solver_iter_500.caffemodel'
params_txt = 'params.txt'
pf = open(params_txt, 'w')
net = caffe.Net(MODEL_FILE, PRETRAIN_FILE, caffe.TEST)
for param_name in net.params.keys():
print(param_... | {
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"""A set of extra ufuncs inspired from PDL: Fused operations.
- add3
- multiply3
- multiply3_add
- multiply_add
- multiply_add2
- multiply4
- multiply4_add
Note: for many use-cases, numba may provide a better solution
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from . im... | {
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import logging
import abc
import logging
import os
import shutil
import struct
import sys
from typing import List, Union, Optional
import numpy as np
from dataforge import Meta
from dataforge.io import JsonMetaFormat
from numpy import dtype
from tables import open_file, File, Group, Filters
from .run_tools import Inp... | {
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from numba.cuda.testing import CUDATestCase, skip_on_cudasim
import subprocess
import sys
import unittest
cuhello_usecase = """\
from numba import cuda
@cuda.jit
def cuhello():
i = cuda.grid(1)
print(i, 999)
print(-42)
cuhello[2, 3]()
cuda.synchronize()
"""
printfloat_usecase = """\
from numba import ... | {
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import argparse
import os
import shutil
from tqdm import tqdm
import logging
import json
import numpy as np
from datasets import load_from_disk,load_metric
from src.utils.all_utils import read_yaml,read_json,parameters,create_directory,has_same_value,quantize_onnx_model,CompressModel
from transformers import AutoTokeni... | {
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"""
Filter 过滤式
"""
from sklearn.feature_selection import VarianceThreshold
from pandas.core.frame import DataFrame
from scipy.stats import pearsonr
from sklearn.decomposition import PCA
import pandas as pd
def vt_test():
"""
方差选择: 选择方差较大的特征
"""
data = [
[-0.46736075, -0.44944782, 0., -0.22941... | {
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import threading
import numpy
import pyglet
from pyglet import gl
from . import visualizer, Visualizer, VisualizerWindow
from ..dsp import bands
class BandsVisualizerWindow(VisualizerWindow):
# TODO:
# Use a shader for rendering and offload some of the calculations into it, this should yield a big
# perfo... | {
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\chapter{Sufficient statistics}
In this chapter the theory of sufficient statistics are introduced.
First we begin with understanding of a statistic. Information about this subject can be found in \cite{casella2002statistical}. A statistic is a function that returns a summary of the data. Examples of this can be mean v... | {
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import os
import sys
import unittest
import numpy as np
import torch
from torch.nn import functional as torch_F
from src.cranet.nn import functional as cranet_F
from src import cranet
from ..utils import teq
class TestRelu(unittest.TestCase):
def test_relu_0(self):
for _ in range(100):
sha... | {
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\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{url}
\usepackage[margin=0.75in]{geometry}
\setlength{\parskip}{0.7em}
\setlength{\parindent}{0em}
\begin{document}
\begin{center}
% MAKE SURE YOU TAKE OUT THE SQUARE BRACKETS
\LARGE{\textbf{CSE 6730, Checkpoint}} \\
\vspace... | {
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module field_mod
use kind_params_mod
use region_mod
use halo_mod
use grid_mod
use gocean_mod, only: gocean_stop
implicit none
private
! Enumeration of grid-point types on the Arakawa C grid. A
! field lives on one of these types.
integer, public, parameter :: U_POINTS = 0
integer, public, para... | {
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import time
import sys;sys.path.append(".")
import numpy as np
vectors1 = np.random.rand(1000, 3)
vectors2 = np.random.rand(1000, 3)
from pikapi.utils.development import compare_function_performances
import pikapi.utils.landmark
import pikapi.landmark_utils
compare_function_performances([
pikapi.landmark_util... | {
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: qiujiarong
# Date: 01/04/2018
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you... | {
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from collections import namedtuple
from typing import Tuple
import numpy as np
from .util import indices_of_binned_phase
from .metrics import _modulation_index
from .signal import Signal
PACResult: Tuple[float] = namedtuple(
"PACResult", "modulation_index mean_phase_coherence"
)
def phase_amplitude_coupling(sa... | {
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"""Tests for graphein.protein.subgraphs"""
# Graphein
# Author: Arian Jamasb <arian@jamasb.io>
# License: MIT
# Project Website: https://github.com/a-r-j/graphein
# Code Repository: https://github.com/a-r-j/graphein
from pathlib import Path
import networkx as nx
import numpy as np
import pytest
from sklearn import ne... | {
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#include "stdafx.h"
#include "CppUnitTest.h"
#include <vector>
#include <algorithm>
#include <list>
#include "../../../iterators.hpp"
#include "../../../math/root.hpp"
#include "../../../math/axis.hpp"
#include "../../../math/multiaxis.hpp"
#include "../../../math/combinatorics.hpp"
#include <boost/math/distributions/n... | {
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//---------------------------------------------------------------------------//
// Copyright (c) 2019-2020 Mikhail Komarov <nemo@nil.foundation>
//
// MIT License
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), ... | {
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# The resilu linearity / non-linearity
The function $resilu(x)=\frac{x}{1-e^{-x}}$ can be written as the sum of a linear funciton and a function that limits to relu(x).
By using resilu(x) and 'non'-linearity in neural networks, the effect of a skip-connection should thus be included 'for free'.
```python
import cop... | {
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"""Module to generate wordclouds from data.
Developed by EricZhu-42 in June, 2019.
"""
import json
import os.path
import matplotlib.pyplot as plt
from scipy.misc import imread
from wordcloud import WordCloud
if __name__ == "__main__":
working_path = os.path.dirname(__file__)
name = r'Frequency_list_of_all'... | {
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[STATEMENT]
lemma (in Worder) ord_isom_Pre1:"\<lbrakk>Worder E; a \<in> carrier D; ExPre D a;
ord_isom D E f\<rbrakk> \<Longrightarrow> ExPre E (f a)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Worder E; a \<in> carrier D; ExPre D a; ord_isom D E f\<rbrakk> \<Longrightarrow> ExPre E (... | {
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# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.13.0
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# display_name: Python 3 (ipykernel)
# language: python
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!##############################################################################
!# ****************************************************************************
!# <name> spacediscretisation </name>
!# ****************************************************************************
!#
!# <purpose>
!# This module contains th... | {
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from collections import OrderedDict
import sys
import os
from decimal import Decimal
import argparse
import copy
import matplotlib.pyplot as plt
from utils import read_sols, write_cfn, read_sim_mat, dissim, get_domain, sols_to_cpd_sols, read_cfn_gzip, ... | {
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import numpy as np
import pickle as pkl
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import torchaudio
import torchvision
import torchvision.models as models
class TomoModel(nn.Module):
def __init__(self):
super(TomoModel, self).__init... | {
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import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
# Set random seed for reproducibility
np.random.seed(1000)
if __name__ == "__main__":
# Create a dummy classification dataset
X, Y = make_classification(n_samples=500, n_classes=5, n_fea... | {
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import os
import pickle
import random
import shutil
import uuid
from pathlib import Path
import numpy as np
class SamplePool:
def __init__(self, location='/tmp/sample_pool', redo=False):
self.location = Path(location)
if self.location.exists() and redo:
shutil.rmtree(str(self.locatio... | {
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#define BOOST_TEST_MODULE AReleaseRPPDU
#include <boost/test/unit_test.hpp>
#include <sstream>
#include <string>
#include "odil/pdu/AReleaseRQ.h"
std::string const data = {
0x05, 0x00,
0x00, 0x00, 0x00, 0x04,
0x00, 0x00,
0x00, 0x00
};
BOOST_AUTO_TEST_CASE(ConstructorFields)
{
odil::pdu::ARelease... | {
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import numpy as np
class Perceptron(object):
def __init__(self, bias=0, eta=0.1, epoch=10):
self.bias = bias
self.eta = eta
self.epoch = epoch
def net_input(self, x):
return self.weights[0] + np.dot(x, self.weights[1:])
def fit(self, X, y):
self.weights = np.z... | {
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from rpy2 import robjects
from typing import Sequence, TypeVar, Union, Dict
import os
from d3m.primitive_interfaces.transformer import TransformerPrimitiveBase
import numpy
from d3m import container
from d3m import utils
from d3m.metadata import hyperparams, base as metadata_module, params
from d3m.primitive_interfaces... | {
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import numpy as np
from exptools2.core import Trial
from psychopy.visual import TextStim
from stimuli import FixationLines
class SomaVisualTrial(Trial):
def __init__(self, session, trial_nr, phase_durations, phase_names,
parameters, timing,
verbose=True, condition='blank'):
... | {
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// big_string_traits.hpp -*- C++ -*-
// Copyright (C) 2013 Martin Trenkmann
#ifndef NETSPEAK_VALUE_BIG_STRING_TRAITS_HPP
#define NETSPEAK_VALUE_BIG_STRING_TRAITS_HPP
#include <cstdio>
#include <string>
#include <boost/algorithm/string/trim.hpp>
#include "netspeak/util/exception.hpp"
#include "netspeak/value/big_stri... | {
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x <= (a);
| {
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"m... |
import jax
import jax.numpy as jnp
import jax.scipy
import numpy as np
from ._su2 import su2_clebsch_gordan, su2_generators
def naive_broadcast_decorator(func):
def wrapper(*args):
args = [jnp.asarray(a) for a in args]
shape = jnp.broadcast_shapes(*(arg.shape for arg in args))
args = [jnp... | {
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# This program is in the public domain
# Author: Paul Kienzle
"""
Random walk functions.
:function:`walk` simulates a mean-reverting random walk.
"""
# This code was developed to test outlier detection
from __future__ import division
__all__ = ["walk"]
from numpy import asarray, ones_like, NaN, isnan
from . import ... | {
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#include <set>
#include <boost/filesystem/fstream.hpp>
#include <boost/program_options.hpp>
#include "picpac-cv.h"
using namespace std;
using namespace picpac;
int main(int argc, char const* argv[]) {
BatchImageStream::Config config;
unsigned max;
float scale;
fs::path db_path;
fs::path dir_path;
... | {
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import numpy as np
import time
# Ray casting
# Dakai Zhou
def TransferFunc1(vol, l1, l2, l3, l4, alpha1, alpha2, alpha3, alpha4, alpha5):
dim = np.shape(vol)
fres = np.zeros([dim[0], 4, dim[1], dim[2]])
res1 = np.zeros([dim[1], dim[2]])
res2 = np.zeros([dim[1], dim[2]])
res3 = np.zeros([dim[1], d... | {
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import numpy as np
from example_robot_data.robots_loader import getModelPath
np.set_printoptions(precision=3, linewidth=200, suppress=True)
LINE_WIDTH = 60
N_SIMULATION = 4000 # number of time steps simulated
dt = 0.002 # controller time step
q0 = np.array([1.73046e-01, -2e-04, -5.25366e-01, 0, 0, 1, 0])
# REFEREN... | {
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import numpy as np
import matplotlib.pyplot as plt
XMIN = 100
XMAX = 700
file = "/media/data/Data/Orderphobic/TwoIntruders/SpikyIntruder/Logging/210121_liquid_{}_flipped_rail.txt"
duties = [590, 600, 610, 620]
fig, ax = plt.subplots(2, 2, sharex=True)
ax = np.ndarray.flatten(ax)
bins = np.arange(XMIN, XMAX)
for i, ... | {
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# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Test different integration methods.
Things not covered here but somewhere else:
* ``conf.set_temp('default_integrator', 'analytical')`` and its default
covered in ``spectrum.rst``.
* The following integrations are tested in ``test_spectrum.py``:
... | {
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#!/usr/bin/env python
import numpy as np
def calculate_charges(waveforms, ped_min, ped_max, sig_min, sig_max):
"""
Calculates the charges of an array of waveforms
Parameters
----------
waveforms: np.array
2D numpy array with one waveform in each row
[[waveform1],
[waveform... | {
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[STATEMENT]
lemma starfun_less_mono:
"\<forall>n. N \<le> n \<longrightarrow> f n < g n \<Longrightarrow> \<forall>n. hypnat_of_nat N \<le> n \<longrightarrow> ( *f* f) n < ( *f* g) n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>n\<ge>N. f n < g n \<Longrightarrow> \<forall>n\<ge>hypnat_of_nat N. (*f* ... | {
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export ±
"""
```julia
±(a,b)
```
- returns (a-b,a+b)
```julia
# Examples
1 ± 0.5 # returns (0.5,1.5)
```
"""
±(a,b) = [a-b,a+b] | {
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"""
@author: A. G. Sreejith
"""
#########################################
### Import Libraries and Functions
#########################################
import os
import numpy as np
import astropy.modeling.functional_models as am
import csc_functions as csc
from astropy.io import ascii
import scipy.speci... | {
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@testset "find_traversal & list_traversal" for t in TEST_TREES
for l in list_traversal(t)
res = find_traversal(t, walk(t, l))
@test all(c -> walk(t, c) === walk(t, l), res)
end
@test isempty(find_traversal(t, Leaf(-1)))
@test isempty(find_traversal(t, [t, t]))
@test isempty(find_trav... | {
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\section{Power Reactor Terminology}
\begin{labeling}
\item [\underline{Coolant}:] Material used to remove heat from core, to
heat water, to push a turbine, etc.
\item [\underline{Steam or Coolant Loops}:] Number of heat transfer mechanisms.
Must be at least 1.
\item [\underline{Moderator}:] ... | {
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//=============================================================================
//
// Copyright (c) Kitware, Inc.
// All rights reserved.
// See LICENSE.txt for details.
//
// This software is distributed WITHOUT ANY WARRANTY; without even
// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
// ... | {
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import torch
import torch.nn as nn
from .steer_pyr_utils import *
class SteerablePyramid(nn.Module):
# refer to https://github.com/LabForComputationalVision/pyrtools
# https://github.com/olivierhenaff/steerablePyramid
def __init__(self, imgSize=[256,256], K=4, N=4, hilb=True, includeHF=True, device=torc... | {
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(* Title: HOL/Induct/Sigma_Algebra.thy
Author: Markus Wenzel, TU Muenchen
*)
section \<open>Sigma algebras\<close>
theory Sigma_Algebra
imports MainRLT
begin
text \<open>
This is just a tiny example demonstrating the use of inductive
definitions in classical mathematics. We define the least \<open... | {
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import numpy as np
import matplotlib.pyplot as plt
import time
import tracemalloc as tm
import psutil
from resource import *
class Stats:
def __init__(self):
self.tic = time.perf_counter()
tm.start()
self.current, _ = tm.get_traced_memory()
def end(self):
_, peak = tm.get_trace... | {
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#include <boost/preprocessor/list/adt.hpp>
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"""
rest.py
quantumML is
Handles the primary functions
"""
from urllib.request import urlopen
import json
import urllib
import os
import io
import math
from pymatgen.core.structure import Structure
from ase.io import vasp
from dscribe.descriptors import SOAP
from pymatgen.io.vasp import Xdatcar, Oszicar
from sklearn.... | {
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#%%
import torch
from model import Model
import numpy as np
import argparse
from utils.pyart import *
from utils.curve import *
from loss import *
from torch.autograd.functional import jacobian
def ForwardNet(model):
def fn(input):
output = model(input)
output = t2p(output)
return output
... | {
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from mutation import mutate, mutate_replace, mutate_insert, mutate_shrink
from node_set import PrimitiveSet, TerminalSet
from tree import generate_tree, parse_tree
from crossover import one_point_crossover
from copy import deepcopy
import numpy as np
import random
if __name__ == '__main__':
# We should use strings... | {
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# -*- coding: utf-8 -*-
r"""
ClusterSeed
A *cluster seed* is a pair `(B,\mathbf{x})` with `B` being a *skew-symmetrizable* `(n+m \times n)` *-matrix*
and with `\mathbf{x}` being an `n`-tuple of *independent elements* in the field of rational functions in `n` variables.
For the compendium on the cluster algebra and qu... | {
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import pybullet as p
import pybullet_data
import pybullet_utils.bullet_client as bc
import numpy as np
import time
import enum
from spatialmath import SE3
from scipy.spatial.transform import Rotation as R
from .pybullet_robot import PyBulletRobot
class GUI_MODE(enum.Enum):
DIRECT = enum.auto()
SIMPLE_GUI = enu... | {
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from __future__ import print_function
import os
import unittest
import numpy
import copy
from baseclasses import BaseRegTest
from pygeo import DVGeometry, geo_utils
class RegTestPyGeo(unittest.TestCase):
N_PROCS = 1
# def setUp(self):
# Store the path where this current script lives
# This ... | {
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# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# 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 us... | {
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import pycuda.autoinit
import pycuda.driver as drv
import numpy
from pycuda.compiler import SourceModule
from jinja2 import Environment, PackageLoader
def main():
#Create dictionary argument for rendering
RenderArgs= {"safe_memory_mapping":1,
"aligned_byte_length_genome":8,
... | {
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# -*- coding: utf-8 -*-
"""
Created on Sat Mar 6 17:11:46 2021
@author: mbeni
"""
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(1000)
#plt.scatter(x, np.ones_like(x))
hist, _ = np.histogram(x, 100)
plt.plot(np.linspace(x.min(), x.max(), 100), hist)
plt.show()
#%%
from scipy.signal i... | {
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"""
This is where you set the run "gtagex" and the initial condition
based on an experiment name passed by the calling code.
"""
import numpy as np
import sys, os
import xarray as xr
from pathlib import Path as pth
path0 = pth.cwd().parent.parent / 'LO_data' / 'grids' / 'cas6' / 'grid.nc'
path1 = pth.cwd().parent / ... | {
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import spikeextractors as si
#import spikewidgets as sw
import spiketoolkit as st
import mlprocessors as mlpr
import json
from cairio import client as ca
import numpy as np
from copy import deepcopy
def compare_sortings_with_truth(sortings,compute_resource,num_workers=None):
print('>>>>>> compare sortings with tru... | {
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//
// FILE NAME: $HeadURL: svn+ssh://svn.cm.aol.com/advertising/adlearn/gen1/trunk/lib/cpp/DataProxy/StreamTransformers/Blackout/private/BlackoutStreamTransformer.cpp $
//
// REVISION: $Revision: 239069 $
//
// COPYRIGHT: (c) 2008 Advertising.com All Rights Reserved.
//
// LAST UPDATED: $Date: 201... | {
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from . import InvertibleModule
import torch
import torch.nn as nn
import numpy as np
def _fast_h(v, stride=2):
"""
Fast product of a series of Householder matrices. This implementation is oriented to the one introducesd in:
https://invertibleworkshop.github.io/accepted_papers/pdfs/10.pdf
This makes us... | {
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from __future__ import absolute_import, division, print_function
import os
import pickle
import tensorflow as tf
from tensorflow import keras
from keras import backend as K
import numpy as np
import tf_util
import gym
import load_policy
import matplotlib.pyplot as plt
from policy import *
def dagger(expert_file, envn... | {
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import numpy as np
class Solution:
def kClosest(self, points: List[List[int]], K: int) -> List[List[int]]:
points.sort(key=lambda P: P[0]**2 + P[1]**2)
return points[:K]
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import argparse
import glob
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from sklearn.datasets import ... | {
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import os
import cv2 ##imports open cv##
import numpy as np
IMAGE_SIZE = (500, 500)
THRESHOLD_VALUE = 110 ##Threshold Details##
MAX_VALUE = 255
INV_THRESHOLD_VALUE = 50 ##Invert Threshold Details##
INV_MAX_VALUE = 255
THRESHOLD1 = 100
THRESHOLD2 = 70
CON_COLOR = (0, 0, 255)
CON_THICKNESS = 1
WHITE = (255, ... | {
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! Copyright (c) 2017-2021, Lawrence Livermore National Security, LLC and
! other Shroud Project Developers.
! See the top-level COPYRIGHT file for details.
!
! From the struct-cxx test reference
module struct_mod
use iso_c_binding, only : C_DOUBLE, C_INT
type, bind(C) :: cstruct1
integer(C_INT) :: ifield
... | {
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/-
Copyright (c) 2020 Johan Commelin. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johan Commelin
-/
import algebra.group_power.lemmas
/-!
# Powers of elements of groups with an adjoined zero element
In this file we define integer power functions for groups with an... | {
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[STATEMENT]
lemma resid_join\<^sub>E [simp]:
assumes "joinable t u" and "v \<frown> t \<squnion> u"
shows "v \\ (t \<squnion> u) = (v \\ u) \\ (t \\ u)"
and "v \\ (t \<squnion> u) = (v \\ t) \\ (u \\ t)"
and "(t \<squnion> u) \\ v = (t \\ v) \<squnion> (u \\ v)"
[PROOF STATE]
proof (prove)
goal (1 subgo... | {
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[STATEMENT]
lemma RELATESI_refspec[refine_dref_pattern]:
"RELATES R \<Longrightarrow> S \<le>\<Down>R S' \<Longrightarrow> S \<le>\<Down>R S'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>RELATES R; S \<le> \<Down> R S'\<rbrakk> \<Longrightarrow> S \<le> \<Down> R S'
[PROOF STEP]
. | {
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'''############
Willard Wider
6/20/18
ELEC 3800
Lab 7
Fourier stuffs
'''############
#importing all the modules we will need
import numpy as np
import scipy.signal as sig
import matplotlib.pyplot as plt
#specify the stype of the plot to use
plt.style.use('ggplot')
#notes
#https://www.mathsisfun.com/calculus/fourier-s... | {
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\newcommand{\norm}[1]{\left\lVert#1\right\rVert}
\newcommand{\KL}[2]{D_{\mathrm{KL}} \bigl( #1 ~||~ #2 \bigr)}
\newcommand{\trans}{\mathbf{T}}
\newcommand{\qex}{Q_{\text{explore}}}
\newcommand{\qtask}{Q_{\text{task}}}
\newcommand{\tex}{\tau_{\text{explore}}}
\newcommand{\ttask}{\tau_{\text{task}}}
\newcommand{\pitask}{... | {
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for :mod:`orion.algo.hyperband`."""
import hashlib
import numpy as np
import pytest
from orion.algo.hyperband.hyperband import Bracket, compute_budgets, Hyperband
from orion.algo.space import Fidelity, Real, Space
@pytest.fixture
def space():
"""Create a S... | {
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import cv2
import numpy as np
import random
from scipy.stats import norm
import matplotlib.pyplot as plt
def generate_spot_light_mask(mask_size,
position=None,
max_brightness=255,
min_brightness=0,
mode="... | {
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#include "RC2Logging.h"
#include <boost/log/core/core.hpp>
#include <boost/log/expressions/formatters/date_time.hpp>
#include <boost/log/expressions.hpp>
#include <boost/log/sinks/sync_frontend.hpp>
#include <boost/log/sinks/text_ostream_backend.hpp>
#include <boost/log/sources/severity_feature.hpp>
#include <boost/lo... | {
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# Author: Teon Brooks <teon.brooks@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
from itertools import chain
import os
import os.path as op
import pytest
import numpy as np
from functools import partial
from string import ascii_lowercase
from numpy.testing import (... | {
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[STATEMENT]
lemma dg_SemiCAT_is_arrE:
assumes "\<FF> : \<AA> \<mapsto>\<^bsub>dg_SemiCAT \<alpha>\<^esub> \<BB>"
obtains "\<FF> : \<AA> \<mapsto>\<mapsto>\<^sub>S\<^sub>M\<^sub>C\<^bsub>\<alpha>\<^esub> \<BB>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<FF> : \<AA> \<mapsto>\<mapsto>\<^sub>S\<^sub>M\<^sub>... | {
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import torch
from torch.autograd import Variable
import numpy as np
from depth_transformation_utils import Rotation, Intrinsics, Translation
from depth_map import DepthMap
from loss import DepthMapTransformation, ReconstructionLoss
device = 'cuda'
def getFrame() :
H, W = 10, 10
intrinsics = Variable(torch.Flo... | {
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#!/usr/bin/python
import os
import sys
import glob
import scipy.stats as stats
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import savemat
from get_qdec_info import get_qdec_info
from fdr import fdr
from exclusion_paradigms import exclParad
qdec_fn = '/users/cais/STUT/FSDATA/qdec/qdec.table.dat'
... | {
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/***************************************************************************
* Software License Agreement (BSD License) *
* Copyright (C) 2017 by *
* Klaus Buchegger <klaus.buchegger@student.tuwien.ac.at> *
* Florian... | {
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
import numpy as np
from functools import partial
import os
from .transformer import Transformer
class DVSA(nn.Module):
def __init__(self, num_class, input_size=2048, enc_size=128, dropout=0.2, hidde... | {
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import os
from src.domain.cargo_space import CargoSpace
from src.model.dataset import Dataset
from src.model.simulation_run_info import SimulationRunInfo
from src.output_option.output_option import OutputOptionInterface
from PIL import Image, ImageDraw, ImageFont
import matplotlib.font_manager as fm
import numpy as n... | {
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# Copyright 2019 ChangyuLiu 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 applica... | {
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import numpy as np
def dataset_vs_testset_checking(data_model, in_dataset):
print("-" * 100)
print("Starting Dataset check")
testing_set = data_model.parameter_range(20, theta_scale_min=None, theta_scale_max=None).cpu().detach().numpy()
for theta in testing_set:
label_array = np.stack(in_datas... | {
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# experimenting with curiosity exploration method.
# Code derived from: https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py
# example command setting args in base_utils.py
# python curiosity.py --models_dir=models-MountainCarContinuous-v0/models_2018_11_28-17-45/ --env="MountainCarCont... | {
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import json
import time
import numpy as np
import requests
import ctypes
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import threading
from Quaternion import *
url = 'http://192.168.1.108:8080/sensors.json'
def getData(url = 'http://192.168.1.108:8080/sensors.json'):
s = requests.Session(... | {
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