text stringlengths 0 27.1M | meta dict |
|---|---|
[STATEMENT]
lemma wadjust_loop_start_Oc_via_Bk_move[simp]:
"wadjust_loop_right_move2 m rs (c, Bk # list) \<Longrightarrow> wadjust_loop_start m rs (c, Oc # list)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wadjust_loop_right_move2 m rs (c, Bk # list) \<Longrightarrow> wadjust_loop_start m rs (c, Oc # list)
[... | {
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# -*- coding: utf-8 -*-
"""
Created on Sun Apr 23 2017
Last update on Mon Apr 24 2017
@author: Michiel Stock
Parsing the Ghent park network
"""
import json
import geopandas as gpd
import shapely
import numpy as np
if __name__=='__main__':
# read roads in Ghent
streets = gpd.read_file('Data/ex_SXXm38nTMVKwsP... | {
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from PIL import Image
import cv2
import matplotlib.pyplot as plt
import pandas as pd
from src.utils import rle_utils as rle
import numpy as np
# Code modified from https://www.kaggle.com/dschettler8845/sartorius-segmentation-mask-dataset#create_dataset
def get_img_and_mask(img_path, annotation, width, height, mask_onl... | {
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import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import Model
tfd = tfp.distributions
tfb = tfp.bijectors
def trainable_lu_factorization(event_size,
batch_shape=(),
seed=... | {
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#coding: utf-8
import pygame
from pygame.locals import *
import random
import sys
#import os
#import codecs
from PIL import Image
import numpy as np
import cv2
# import module that I made
import faceCamera
import detectFace
CS = 6 # cell size
SCR_RECT = Rect(0, 0, 6*int(800/6), 6*int(800/6)) # screen size depends on... | {
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import numpy as np
import tensorflow as tf
from ..core.image_warp import image_warp
class ImageWarpTest(tf.test.TestCase):
def _warp_test(self, first, second, flow, debug=False):
num_batch, height, width, channels = second.shape
second_ = tf.placeholder(tf.float32, shape=second.shape, name='im')
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import numpy as np
import pandas as pd
import theano
import theano.tensor as tt
def get_variational_scores(result, config, model, inference, true_pop_size):
approx_params = list(inference.approx.shared_params.values())
distance = abs(model.pop_size - true_pop_size)/true_pop_size
input_vars = tt.dvectors(le... | {
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function loadData(path::String)
datasets = DataFrame[] # Init empty vector
if ispath(path)
if isfile(path)
push!(datasets, CSV.read(path))
return datasets
else
files = readdir(path) # grab all files in the given folder
for f in files
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@reexport module Maze
export MazeEnv
using ReinforcementLearningBase
import Base: *
const Actions = [
CartesianIndex(0, -1), # left
CartesianIndex(0, 1), # right
CartesianIndex(-1, 0), # up
CartesianIndex(1, 0), # down
]
mutable struct MazeEnv <: AbstractEnv
walls::Set{CartesianIndex{2}}
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"""
Generate sample x, y, data of hierarchical structure for bebi103
documentation.
"""
import numpy as np
import pandas as pd
def _generate_sample_data():
np.random.seed(3252)
J_1 = 3
n = np.array([20, 25, 18])
theta = np.array([3, 7])
tau = np.array([1, 4])
sigma = np.array([2, 3])
rho ... | {
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from ml_logger import logger
from cde.model_fitting.GoodnessOfFitResults import GoodnessOfFitResults
from cde.evaluation.simulation_eval import base_experiment
import cde.model_fitting.ConfigRunner as ConfigRunner
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
EXP_PREFIX = 'question1_noise_re... | {
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#!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Test layers in modules.autoregressive.
"""
# =================================================================... | {
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struct LinearInterpolant{T,N}
y::Array{T,N}
Δx::Float64
end
function (f::LinearInterpolant)(a::Float64)
Δx = f.Δx
s = a/(Δx);
n₁ = floor(Int64,s);
n₂ = ceil(Int64,s);
Δn = (s - n₁)
(1-Δn)*f.y[n₁+1] + Δn*f.y[n₂+1]
end
function qnms(;l=0,m=0,n=0,s=0,amax=0.99, ϵ = 0.01)
as, ωs, Alms... | {
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# -*- coding: utf-8 -*-
"""
Created on Wed Apr 5 16:00:11 2017
@author: lracuna
"""
import numpy as np
import cv2
import glob
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*7,3... | {
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str="Data Science" ;str
str1='Data Science';str1
str2="Teacher guide's";str2
str3='Teacher guide"s';str3
str4="Data Science using R"
str5='Data Science using python'
paste("Hello","World",sep="$")
paste(str1,str2,str3,str4,str5,sep=" ")
paste(c("something","go's","wrong"),"in LPU",sep="+",collapse="#")
for... | {
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from collections import OrderedDict
from tempfile import TemporaryDirectory
from typing import (
Tuple,
Union,
List,
Iterable,
Dict,
Any,
Type,
Callable,
Optional,
Sequence,
)
import numpy as np
import torch
from nebullvm import optimize_torch_model
from nebullvm.api.frontend.u... | {
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C$PRAGMA SUN OPT=2
subroutine tabgen
c $Id$
c
c ***** computes and tabulates f0(x) to f5(x) *****
c ***** in range x = -0.24 to x = 26.4 *****
c ***** in units of x = 0.08 *****
c ***** the two electron integral sp routines ... | {
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# Copyright 2020 D-Wave Systems Inc.
#
# 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 w... | {
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# ===========================================
#
# mian Heatmap Library
# @author: tbj128
#
# ===========================================
#
# Imports
#
from scipy.stats import stats
from mian.model.otu_table import OTUTable
import numpy as np
from mian.analysis.alpha_diversity import AlphaDiversity
class Heatmap(obj... | {
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# This file conists of the functions which are used in SLMC/Restricted-SLMC training.
import numpy as np
import numpy.random as rnd
from sklearn import linear_model
from Configuration import Configuration
from Hamiltonian import first_NN_interaction, second_NN_interaction, third_NN_interaction
from LocalUpdate import ... | {
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ctp 7/27/02 reverse the arguments in arg
c ----------------------------------------------------------------------
subroutine ANGULAR_ARRAY_LE_QG(massin,NSANG)
implicit none
integer n,n1,n2,n3,n4,n5,ndim
real*8 theta_s3
real*8 arg(1:3,0:9,1:3),arg_x(0:9),arg_y(0:9)
real*8 NSANG(0:9,... | {
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from second_step_on_vertex_visit import second_step_on_vertex_visit
import numpy as np
import pandas as pd
from initialize_graph import Vertex, build_graph, find_shortest_path
from initialize_graph import Robot
from collections import defaultdict
import networkx as nx
import matplotlib.pyplot as plt
from first_step_on... | {
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/*
* Copyright (c) 2013 Juniper Networks, Inc. All rights reserved.
*/
#include "bgp/routing-instance/service_chaining.h"
#include <boost/foreach.hpp>
#include <algorithm>
#include "base/task_annotations.h"
#include "base/task_trigger.h"
#include "bgp/bgp_config.h"
#include "bgp/bgp_log.h"
#include "bgp/bgp_peer_... | {
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import numpy as np
from second.core.anchor_generator import (
AnchorGeneratorStride, AnchorGeneratorRange)
def build(anchor_config):
"""Create optimizer based on config.
Args:
optimizer_config: A Optimizer proto message.
Returns:
An optimizer and a list of variables for summary.
Raises:
... | {
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# -------------------------------------------------------------------------------
# Copyright IBM Corp. 2016
#
# 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/licens... | {
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!*****************************************************************************************
!> author: GasinAn
!
! Simplified Modern Fortran Edition of the DOP853 ODE Solver.
!
!### License
!
! Simplified Modern Fortran Edition of the DOP853 ODE Solver
! https://github.com/GasinAn/easydop853
!
!... | {
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struct TikzFigure <: AbstractTikzFigure
axes::Vector{AbstractTikzAxis}
end
function TikzFigure()
return TikzFigure([EmptyTikzAxis()])
end
function EmptyTikzFigure()
return TikzFigure()
end
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[STATEMENT]
lemma times_inf [simp]:
"x * y = x \<sqinter> y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x * y = x \<sqinter> y
[PROOF STEP]
by simp | {
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abstract type AbstractBoolDomain <: AbstractDomain end
"""
struct BoolDomain <: AbstractDomain
Boolean domain, uses a IntDomain in it. (true is 1 and false is 0)
"""
struct BoolDomain <: AbstractBoolDomain
inner::IntDomain
function BoolDomain(trailer::Trailer)
return new(IntDomain(tr... | {
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[STATEMENT]
lemma lspasl_starr_der:
"(h1,h2\<triangleright>h0) \<Longrightarrow> \<not> ((A ** B) h0) \<Longrightarrow>
((h1,h2\<triangleright>h0) \<and> \<not> ((A h1) \<or> ((A ** B) h0)) \<and> (starr_applied h1 h2 h0 (A ** B))) \<or>
((h1,h2\<triangleright>h0) \<and> \<not> ((B h2) \<or> ((A ** B) h0)) \<an... | {
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import os
import sys
import json
import pickle
import random
import torch
# from torch.utils.tensorboard.summary import image
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pylab as pl
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import torchvision.transforms.function... | {
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import argparse
import asyncio
import functools
import io
import json
import re
import subprocess
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from threading import Thread
import janus
import numpy as np
import tesserocr
import websockets
from skimage.color import rgb2gray
fr... | {
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# Pipeline.py
# Author: Marcus D. Bloice <https://github.com/mdbloice>
# Licensed under the terms of the MIT Licence.
"""
The Pipeline module is the user facing API for the Augmentor package. It
contains the :class:`~Augmentor.Pipeline.Pipeline` class which is used to
create pipeline objects, which can be used to buil... | {
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import numpy as np
import cv2
import matplotlib.pyplot as plt
from scipy.fftpack import fft2, ifft2, fftshift, ifftshift
from scipy.stats import multivariate_normal
from scipy.ndimage import rotate
from .image_io import crop_patch
from .utils import pre_process, rotateImage, plot
######################################... | {
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// example/stopwatch_example.cpp ---------------------------------------------------//
// Copyright Beman Dawes 2006, 2008
// Copyright 2009-2011 Vicente J. Botet Escriba
// Distributed under the Boost Software License, Version 1.0.
// See http://www.boost.org/LICENSE_1_0.txt
// See http://www.boost.org/libs/chr... | {
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[STATEMENT]
lemma antisymPI: "(\<And>x y. \<lbrakk> r x y; r y x \<rbrakk> \<Longrightarrow> x = y) \<Longrightarrow> antisymp r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>x y. \<lbrakk>r x y; r y x\<rbrakk> \<Longrightarrow> x = y) \<Longrightarrow> antisymp r
[PROOF STEP]
by (fact antisympI) | {
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[STATEMENT]
lemma assign_eval\<^sub>w_const\<^sub>C:
shows "(\<langle>x \<leftarrow> Const c, mds, mem\<rangle>, \<langle>Stop, mds, mem (x := c)\<rangle>) \<in> C.eval\<^sub>w"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. eval_abv\<^sub>C \<langle>x \<leftarrow> aexp\<^sub>C.Const c, mds, mem\<rangle>\<^sub>C \... | {
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import numpy as np
from numba import cuda, int32, float32
from numba.cuda.testing import unittest, CUDATestCase
from numba.core.config import ENABLE_CUDASIM
def useless_sync(ary):
i = cuda.grid(1)
cuda.syncthreads()
ary[i] = i
def simple_smem(ary):
N = 100
sm = cuda.shared.array(N, int32)
i ... | {
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from flearn.servers.server import Server
import numpy as np
class qFFL(Server):
def __init__(self, q, L, train_data, ids, Learner, initial_params, learning_rate):
self.L = L
self.q = q
super(qFFL, self).__init__(train_data, ids, Learner, initial_params, learning_rate)
def trai... | {
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% DEMSPGP1D2 Do a simple 1-D regression after Snelson & Ghahramani's example.
% GP
% Fix seeds
randn('seed', 2e5);
rand('seed', 2e5);
seedVal = 2e5;
dataSetName = 'spgp1d';
experimentNo = 2;
% load data
[X, y] = mapLoadData(dataSetName, seedVal);
% Set up model
options = gpOptions('fitc');
options.numActive = 9;
%... | {
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FFT_TYPE = "scipy"
import os
import pathlib
import warnings
import numpy as np
import scipy.signal
from scipy.io import wavfile
from ..parameter import Parameter
from ..processor import Processor
from ..parameter_list import ParameterList
if FFT_TYPE == "scipy":
from scipy.fftpack import fft, ifft
else: ... | {
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# -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | {
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# -*- coding: utf-8 -*-
from ..io.spec import spec
import h5py
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
from ..math.utils import logscale
import warnings
def show(
x,
y,
images,
xp,
yp,
xlabel,
ylabel,
names,
transpose=False,
flipvert=Fa... | {
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Require Import ssr.
Require Import lib.
Require Import withzero.
Set Implicit Arguments.
Unset Strict Implicit.
Import Prenex Implicits.
Open Scope dnat_scope.
Module Type GALOIS.
(* -------------------------------------------------------------------------- *)
(* Rings ... | {
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[STATEMENT]
lemma read_point:
assumes "point p"
and "mapping x"
shows "point (x[[p]])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. point (x[[p]])
[PROOF STEP]
using assms comp_associative read_injective read_surjective
[PROOF STATE]
proof (prove)
using this:
point p
coreflexive (x[[x]]) \<and> times_top_c... | {
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#include <Eigen/Dense>
#include <iostream>
#include <fstream>
#include <boost/dynamic_bitset.hpp>
#include <boost/container/vector.hpp>
#include <boost/unordered_map.hpp>
#include <boost/random/uniform_01.hpp>
#include <boost/random/niederreiter_base2.hpp>
#include <./Timer.cpp>
typedef boost::dynamic_bitset<> Vetor;
... | {
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\subsection{Surface integral for vector fields}
| {
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2019-12-04 15:13 qiang.zhou <theodoruszq@gmail.com>
#
# Distributed under terms of the MIT license.
"""
"""
import cv2
from PIL import Image
import random
import numpy as np
import torch
import torchvision.transforms.functional as TF
de... | {
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using Polyhedra
include("simplex.jl")
include("permutahedron.jl")
include("board.jl")
myeq(x::Real, y::Real) = myeq(promote(x, y)...)
myeq{T<:Real}(x::T, y::T) = x == y
myeq{T<:AbstractFloat}(x::T, y::T) = y < x+1024*eps(T) && x < y+1024*eps(T)
myeq{S<:Real,T<:Real}(x::Vector{S}, y::Vector{T}) = myeq(promote(x, y)...... | {
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import rc # TO MANAGE MATPLOTLIB PARAMETERS"
rc('font',family='serif')
rc('text',usetex = True)
import scipy.optimize as optimization
logeVe,logr,logAcce,logtcelle,logAdve,logDiffe,logEmaxHe,logSye,logIC,logBr = np.loadtxt('e... | {
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"""
This module contains the tests for timeconv function
"""
# Standard library imports
# Third party imports
from pytest import approx
#https://www.scivision.dev/pytest-approx-equal-assert-allclose/
import numpy as np
from pathlib import Path
import sys
from numpy import rad2deg, deg2rad
# Local application import... | {
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import sys
import json
from pathlib import Path
import logging
import gc
import click
import numpy as np
import torch
from data import Vocab, Dataset
from model import MultiClassModel
from config import Config
logger = logging.getLogger(__name__)
LOG_FORMAT = '[%(asctime)s] [%(levelname)s] %(message)s (%(funcName)s@... | {
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import numpy as np
import matplotlib.pyplot as plt
def plot_nodes(graph, color='r'):
for node in range(len(graph.x_of_node)):
x, y = graph.x_of_node[node], graph.y_of_node[node]
plt.plot(graph.x_of_node[node], graph.y_of_node[node], 'o',
color=color)
plt.text(x, y, node, c... | {
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import time
import numpy as np
import vpi
import cv2
from threading import Thread
from PIL import Image
from jetvision.elements import Camera
MAX_DISP = 64
WINDOW_SIZE = 10
def get_calibration() -> tuple:
fs = cv2.FileStorage(
"calibration/rectify_map_imx219_160deg_1080p.yaml", cv2.FILE_STORAGE_READ
... | {
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.
"""
signal
"""
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import numpy as np
from onnx import onnx_pb
from onnx.numpy_helper import to_array... | {
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# -------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ----------------------------------------------------------------------... | {
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#pragma once
#include <Eigen/Dense>
#include <Eigen/Sparse>
#include <cstdlib>
namespace edp
{
template<typename T, class ColFunc>
auto constructMat(size_t dim, ColFunc&& colFunc) -> Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
{
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> res(dim, dim);
res.setZero(... | {
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Glacier Point Apartments is a quiet and friendly community located in an optimal area of West Davis. Residents enjoy a fitness center, pool and spa, barbeque spot, media loft and internet lounge with free WiFi. With quick freeway access, walking distance to shopping, restaurants and more, and a bike path and bus li... | {
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"ext": "f",
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"hexsha": "37d56945d91fbcc4517edac807c9bd55455da093",
"include": null,
"lang": "FORTRAN",
"length": null,
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#!/usr/bin/env python
# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4 ai :
"""Identify and flag sources as either stellar sources, extended sources or anomalous sources
Anomalous sources fall into several categories:
- Saturated sources: the pixel values in the cores of these sources are maxed out at the detect... | {
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/*
* Copyright 2014 Antony Polukhin
* Copyright 2015 Andrey Semashev
*
* Distributed under the Boost Software License, Version 1.0.
* See http://www.boost.org/LICENSE_1_0.txt
*/
#ifndef BOOST_WINAPI_CRYPT_HPP_INCLUDED_
#define BOOST_WINAPI_CRYPT_HPP_INCLUDED_
#include <boost/winapi/basic_types.hpp>
#include <bo... | {
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from ..data import DATA_PATH
from .. import simple_cov
import pytest
from pyuvdata import UVData
import os
import numpy as np
@pytest.fixture
def sky_model():
uvd = UVData()
uvd.read_uvh5(
os.path.join(
DATA_PATH,
"Garray_antenna_diameter2.0_fractional_spacing1.0_nant6_nf200_df... | {
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"""
Forced DA Analysis
------------------
Top-level script to run the forced DA analysis, following the procedure described in
`CarlierForcedDA2019`_.
Arguments:
*--Required--*
- **beam** *(int)*: Beam to use.
Flags: **['-b', '--beam']**
Choices: ``[1, 2]``
- **energy** *(MultiClass)*: Beam energy in GeV.
F... | {
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import os
import scipy.io
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch
import time
import sys
sys.path.insert(0, '../prroi_pool')
from modules.prroi_pool import PrRoIPool2D
def append_params(params, mo... | {
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"include": true,
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import functools
import pickle
import random
from baselines.common.vec_env import VecEnvWrapper
import gym
import numpy as np
import os.path as osp
import tensorflow as tf
from rllab.envs.base import Env, EnvSpec
import rllab.misc.logger as rl_logger
from sandbox.rocky.tf.envs.base import TfEnv, to_tf_space
from rlla... | {
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[STATEMENT]
lemma scene_union_foldr_remove_element:
assumes "set xs \<subseteq> set Vars"
shows "a \<squnion>\<^sub>S \<Squnion>\<^sub>S xs = a \<squnion>\<^sub>S \<Squnion>\<^sub>S (removeAll a xs)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<squnion>\<^sub>S \<Squnion>\<^sub>S xs = a \<squnion>\<^sub>S ... | {
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import torch as tc
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertModel , BertTokenizer
import pdb
import math
def loss_1(pred , anss , ents , no_rel , class_weight , pad_ix = -100):
'''
直接平均，按类别加权
and unweighted avg
'''
import numpy as np
bs , ne , _ , d = pred.size()
if... | {
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import numpy as np
import itertools
from .displacements import Displacements
from .kvectors import Kvectors
class Lattice():
"""Class to generate the lattice."""
__vecsLattice = np.array([], dtype=np.float)
__vecsBasis = np.array([], dtype=np.float)
__idxBasis = np.array([])
__idxSub = np.array(... | {
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# Created byMartin.cz
# Copyright (c) Martin Strohalm. All rights reserved.
import numpy
# comparison with tolerance
def equals(v1, v2, epsilon):
"""
Returns True if difference between given values is less then tolerance.
Args:
v1: float
Value one.
v2: float
... | {
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\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{parskip}
\usepackage{svg}
\usepackage[utf8]{inputenc}
\usepackage{helvet}
\renewcommand{\familydefault}{\sfdefault}
\usepackage{geometry}
\usepackage[document]{ragged2e}
\geometry{letterpaper, portrait, top=1in, bottom=1in, left=1.5in, right... | {
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#include"Character.h"
#include "Kobieta.h"
#include "Julek.h"
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(CharacterTest)
Kobieta K(32,32,5);
Julek J(56,78,8);
BOOST_AUTO_TEST_CASE(KobietaInitializingLivesChecking) {
BOOST_CHECK_EQUAL(K.getLives(),5);
}
BOOST_AUTO_TEST_CASE(KobietaIniti... | {
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#! /usr/bin/env python
"""
Phase_function class definition
"""
from __future__ import division, print_function
__author__ = 'Julien Milli'
__all__ = []
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
class Phase_function(object):
""" This class represents the scatterin... | {
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[STATEMENT]
lemma empty_fv_exists_fun: "fv t = {} \<Longrightarrow> \<exists>f X. t = Fun f X"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fv t = {} \<Longrightarrow> \<exists>f X. t = Fun f X
[PROOF STEP]
by (cases t) auto | {
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... |
import pathlib
import torch
import numpy as np
import gvision_utils
import img_utils
class PretrainedModel():
def __init__(self, modelname):
model_pt = model_class_dict[modelname](pretrained=True)
#model.eval()
self.model = nn.DataParallel(model_pt.cuda())
self.model.eval()
... | {
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__precompile__()
module DataProcessingHierarchyTools
using ProgressMeter
using Glob
using MAT
using StrTables, LaTeX_Entities
using StableHashes
import StableHashes.shash
import Base:filter,show, convert
include("types.jl")
const def = LaTeX_Entities.default
function git_annex()
cmd = nothing
try
cmd... | {
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(* Default settings (from HsToCoq.Coq.Preamble) *)
Generalizable All Variables.
Unset Implicit Arguments.
Set Maximal Implicit Insertion.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
Require Coq.Program.Tactics.
Require Coq.Program.Wf.
(* Preamble *)
Require String BitTerminationProofs.
Import String.... | {
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[STATEMENT]
lemma monad_alt_optionT' [locale_witness]:
"monad_alt return (bind :: ('a option, 'm) bind) alt
\<Longrightarrow> monad_alt return (bind :: ('a, ('a, 'm) optionT) bind) alt"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. monad_alt Monad_Overloading.return Monad_Overloading.bind alt \<Longrightarrow> ... | {
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import gym
import numpy as np
import dso.task.control # Registers custom and third-party environments
from dso.program import Program, from_str_tokens
from dso.library import Library
from dso.functions import create_tokens
import dso.task.control.utils as U
REWARD_SEED_SHIFT = int(1e6) # Reserve the first million s... | {
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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from collections import Counter
import os
from argparse import Namespace
flags = Namespace(
train_file='dane_disco.txt',
seq_size=3,
batch_size=120,
embedding_size=84,
lstm_size=384,
g... | {
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/**********************************************************************
* Copyright (c) 2008-2014, Alliance for Sustainable Energy.
* All rights reserved.
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published ... | {
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'''
All the functions linked to the prediction behaviour of the robot
'''
import algebra as alg
from math import sqrt,cos,sin,acos,pi,atan2
import numpy as np
deltaT = 0.4 #s
def predictionNextPosition(linearSpeed,position):
'''
Return the predicted next position of the robot
Considering... | {
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import tensorflow as tf
from tensorflow.contrib.layers import xavier_initializer
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
import imageio
initializer = xavier_initializer()
# 为生成器生成随机噪声
Z = tf.placeho... | {
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import unittest
import numpy as np
from sklearn.metrics import pairwise_kernels
from skactiveml.pool._quire import (
_del_i_inv,
_L_aa_inv,
_one_versus_rest_transform,
Quire,
)
from skactiveml.utils import MISSING_LABEL, is_labeled, is_unlabeled
class TestQuire(unittest.TestCase):
def setUp(self... | {
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import numpy as np
import spikeextractors as se
class OutputRecordingExtractor(se.RecordingExtractor):
def __init__(self, *, base_recording, block_size):
super().__init__()
self._base_recording = base_recording
self._block_size = block_size
self.copy_channel_properties(recording=sel... | {
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[STATEMENT]
lemma has_white_path_to_refl[iff]:
"(x has_white_path_to x) s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x has_white_path_to x) s
[PROOF STEP]
unfolding has_white_path_to_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>x y. (x points_to y) s \<and> white y s)\<^sup>*\<^sup>* x x
[... | {
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import torch
import numpy as np
from sklearn.mixture import GaussianMixture
from torch import nn
import os
from torch.utils.data import DataLoader
from sklearn.cluster import KMeans
from torch.autograd import Variable
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from python_research.io ... | {
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//=======================================================================
// Copyright 2001 University of Notre Dame.
// Copyright 2006 Trustees of Indiana University
// Authors: Jeremy G. Siek and Douglas Gregor <dgregor@cs.indiana.edu>
//
// Distributed under the Boost Software License, Version 1.0. (See
// acc... | {
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import gym
import numpy as np
import random
import simple_memory_testing_env
import matplotlib.pyplot as plt
rotate_right = 1
rotate_left = 0
forward = 2
def test_env():
env = gym.make(f"SimpleMemoryTestingEnv-v0")
obs = env.reset()
#env.render()
obs = env.step(forward)
obs = env.step(... | {
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[STATEMENT]
lemma Exit_not_dyn_standard_control_dependent:
assumes control:"n controls\<^sub>s (_Exit_) via as" shows "False"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. False
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. False
[PROOF STEP]
from control
[PROOF STATE]
proof (chain)
picki... | {
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"ma... |
/* *****************************************************
THIS IS AN AUTOMATICALLY GENERATED FILE. DO NOT EDIT.
*****************************************************
Generated by:
gltbx.generate_defines_bpl
*/
#include <boost/python/def.hpp>
#include <boost/python/scope.hpp>
#include <gltbx/include_open... | {
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import pickle
import os
import torch
import time
import numpy as np
from torch import nn
import matplotlib.pyplot as plt
from regretnet import ibp
from regretnet.mipcertify.mip_solver import MIPNetwork
from regretnet.regretnet import RegretNet, calc_agent_util, optimize_misreports, tiled_misreport_util
from regretnet.... | {
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import scipy.io as sio
import numpy as np
from skimage import data, io
import cv2
M = sio.loadmat('M.mat')
M = M['M']
# cv2.imshow('M', M)
# cv2.waitKey(0)
#
# A = cv2.imread('kodim11.JPG', 3)
# print type(A)
# cv2.imshow('Aasd', A)
# cv2.waitKey(0)
Dict = sio.loadmat('Dict.mat')
Dict = Dict['Dict']
# normalized = np... | {
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"""Reads the text data stored as sparse matrix."""
import numpy as np
import scipy.sparse as sp
from sklearn.model_selection import train_test_split
import pandas as pd
def removeFirstColumn(data):
new_data = []
for i in range(len(data)):
new_data.append(data[i][1:])
new_data = np.array(new_data)
... | {
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[STATEMENT]
lemma satisifies_atom_restrict_to_Cons: "v \<Turnstile>\<^sub>a\<^sub>s restrict_to I (set as) \<Longrightarrow> (i \<in> I \<Longrightarrow> v \<Turnstile>\<^sub>a a)
\<Longrightarrow> v \<Turnstile>\<^sub>a\<^sub>s restrict_to I (set ((i,a) # as))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbr... | {
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import numpy as np
from sympy import symbols
from . import example_smooth_reservoir_models as ESRM
from .smooth_model_run import SmoothModelRun
def critics():
symbs = symbols("t k_01 k_10 k_0o k_1o")
t, k_01, k_10, k_0o, k_1o = symbs
srm = ESRM.critics(symbs)
pardict = {k_0o: 0.01, k_1o: 0.08, k_01: ... | {
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import numpy as np
class TransReplayBuffer(object):
def __init__(self, size):
self.size = size
self.buffer = []
def get_single(self, index):
return self.buffer[index]
def offset(self):
self.buffer.pop(0)
def get_batch(self, batch_size):
return self.get_trunc... | {
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\chapter{Additional link functions for neural networks}
| {
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#!/usr/bin/env python
import numpy
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def euclideanDist(p1, p2):
from math import sqrt
return sqrt((p1.x-p2.x)**2 + (p1.y-p2.y)**2)
def getMinDist(p1, precision=0.001, startX=0, endX=3):
"""Get x of point on (x,x^2) that has m... | {
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/*
+----------------------------------------------------------------------+
| HipHop for PHP |
+----------------------------------------------------------------------+
| Copyright (c) 2010-2013 Facebook, Inc. (http://www.facebook.com) |
+---------... | {
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# -*- coding: utf-8 -*-
import numpy as np
import os
from knn_practice.knn_1 import classify0
# 把32✖️32的二进制图像矩阵转换为1✖️1024的向量
def img2vector(filename):
return_vect = np.zeros((1, 1024))
with open(filename) as fr:
for i in range(32):
line_str = fr.readline()
for j in range(32):
... | {
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"""
This script defines some functions used for visualize the final results.
"""
import numpy as np
import matplotlib.pyplot as plt
import torch
from modules.model import Net
def import_best_model(best_model, category, phi, theta):
"""
Argumnets:
best_model: .pth file containing model parameters
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