text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
def plot_1d(X_train, Y_train, X_test, Y_test, mean=None, std=None, str_figure=None, show_fig=True):
plt.rc('text', usetex=True)
fig = plt.figure(figsize=(8, 6))
ax = fig.gca()
ax.plot(X_test, Y_... | {"hexsha": "363f98c059fbed994ba92f98a94c9d889c901242", "size": 2518, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "jungtaekkim/On-Uncertainty-Estimation-by-Tree-based-Surrogate-Models-in-SMO", "max_stars_repo_head_hexsha": "de195a391f1f9bfc4428dadda9400850408e88ca", "max_... |
using Test, YaoBlocks, YaoArrayRegister, LuxurySparse
@testset "test constructor" for T in [Float16, Float32, Float64]
@test PhaseGate(0.1) isa PrimitiveBlock{1}
@test_throws TypeError PhaseGate{Complex{T}} # will not accept non-real type
@test phase(T(0.1)) isa PrimitiveBlock{1}
@test phase(1) isa Ph... | {"hexsha": "89dfc761ace07b8bdbedcec9cadf06e097694a44", "size": 1361, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/primitive/phase_gate.jl", "max_stars_repo_name": "yihong-zhang/YaoBlocks.jl", "max_stars_repo_head_hexsha": "9bd8f309b5c258968fb5ce4c2f12fc5e854d8b68", "max_stars_repo_licenses": ["Apache-2.0"... |
SUBROUTINE iau_ATIOQ ( RI, DI, ASTROM, AOB, ZOB, HOB, DOB, ROB )
*+
* - - - - - - - - - -
* i a u _ A T I O Q
* - - - - - - - - - -
*
* Quick CIRS to observed place transformation.
*
* Use of this routine is appropriate when efficiency is important and
* where many star positions are all to be transformed ... | {"hexsha": "26e28817b7b8ab390326a55da27231e11168d068", "size": 11476, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "f77/src/atioq.for", "max_stars_repo_name": "berke/sofa_f", "max_stars_repo_head_hexsha": "312be79467c4adbe344e45f9d326d1e89fea5cd4", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count":... |
[STATEMENT]
lemma effect_LetE [effect_elims]:
assumes "effect (let x = t in f x) h h' r"
obtains "effect (f t) h h' r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (effect (f t) h h' r \<Longrightarrow> thesis) \<Longrightarrow> thesis
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
effect (Le... | {"llama_tokens": 179, "file": null, "length": 2} |
# SPDX-FileCopyrightText: Copyright 2021, Siavash Ameli <sameli@berkeley.edu>
# SPDX-License-Identifier: BSD-3-Clause
# SPDX-FileType: SOURCE
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the license found in the LICENSE.txt file in the root
# directory of this source ... | {"hexsha": "cfadfe06abfc5c7ad7f645d98f7e0bd8df5fd97a", "size": 5191, "ext": "py", "lang": "Python", "max_stars_repo_path": "imate/traceinv/_convergence_tools.py", "max_stars_repo_name": "ameli/TraceInv", "max_stars_repo_head_hexsha": "fe759af016c5845d88ca98e56994b6c86c132792", "max_stars_repo_licenses": ["BSD-3-Clause"... |
##########################################
# 1-dimensional latent and 1PL pars #
##########################################
"""
```julia
__probability(latent_val::Float64, parameters::Parameters1PL)
```
# Description
It computes the probability (ICF) of a correct response for item `parameters` under the 1PL mode... | {"hexsha": "344169fd3a975f01e4bf7ca792e689f36284aae0", "size": 14574, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/probability.jl", "max_stars_repo_name": "giadasp/Psychometrics.jl", "max_stars_repo_head_hexsha": "4d4046dfc235ffc8045d4a43d9a6d003930dbb60", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma bin_rep_coeff:
fixes n m i:: nat
assumes "m < 2^n" and "i < n" and "m \<ge> 0"
shows "bin_rep n m ! i = 0 \<or> bin_rep n m ! i = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bin_rep n m ! i = 0 \<or> bin_rep n m ! i = 1
[PROOF STEP]
using assms bin_rep_def bin_rep_aux_coeff length_of_bi... | {"llama_tokens": 361, "file": "Isabelle_Marries_Dirac_Binary_Nat", "length": 2} |
# Module to run tests on generating IGMSystem
from __future__ import print_function, absolute_import, division, unicode_literals
# TEST_UNICODE_LITERALS
import pytest
from astropy import units as u
from astropy.coordinates import SkyCoord
import numpy as np
from pyigm.abssys.igmsys import IGMSystem, HISystem
impor... | {"hexsha": "b3a873847e810b7d30966b791c38ca9e437616c6", "size": 752, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyigm/abssys/tests/test_init_igmsys.py", "max_stars_repo_name": "pyigm/pyigm", "max_stars_repo_head_hexsha": "8b4bc7f7f1c9f1c280720a4cc0693cd7cb79e9cb", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
[STATEMENT]
lemma sub_trees_refl[simp]: "t \<in> sub_trees t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t \<in> sub_trees t
[PROOF STEP]
by (cases t, auto) | {"llama_tokens": 73, "file": "Berlekamp_Zassenhaus_Hensel_Lifting", "length": 1} |
function V = load_images(params)
sequence_name = params.sequences_name(params.current_sequence).name;
path = fullfile(params.sequences_path,sequence_name,params.sequences_format);
list = dir(fullfile(path,params.sequences_ext));
names = char({list.name}');
frames = size(names,1);
V = [];
for i = 1:frames
... | {"author": "andrewssobral", "repo": "mctc4bmi", "sha": "fbcbcd25654b818646387c3d6a64304fb60e12dd", "save_path": "github-repos/MATLAB/andrewssobral-mctc4bmi", "path": "github-repos/MATLAB/andrewssobral-mctc4bmi/mctc4bmi-fbcbcd25654b818646387c3d6a64304fb60e12dd/load_images.m"} |
import tensorflow as tf
import numpy as np
from math import ceil
from copy import deepcopy
from tensorflow.examples.tutorials.mnist import input_data
import random
import matplotlib.pyplot as plt
def permute_mnist(mnist,per_task):
perm_inds = list(range(mnist.train.images.shape[1]))
random.seed(per_task)
... | {"hexsha": "70100e9d4481aa4b1f71e7ef0f8c5eaceff462e4", "size": 11350, "ext": "py", "lang": "Python", "max_stars_repo_path": "Permuted_MNIST/ANPyC_wo_NP.py", "max_stars_repo_name": "GeoX-Lab/ANPSC", "max_stars_repo_head_hexsha": "9dbab0569161f4a1b51841c75e0517ee41672639", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
SUBROUTINE GG_TCSH ( xlat1, xlon1, np1, xlat2, xlon2, np2,
+ ylat, ylon, npts, iret )
C************************************************************************
C* GG_TCSH *
C* *
C* This subroutine calculates the bound of two intersecting polygons. *
C* It works with polygons which are def... | {"hexsha": "d7d2d32e8b99e51c34fee3f7e7d1a95d80f87465", "size": 9154, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/gemlib/gg/ggtcsh.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
"""This module contains all the stress models that available in
Pastas. Stress models are used to translate an input time series into a
contribution that explains (part of) the output series.
Supported Stress models
-----------------------
The following stressmodels are currently supported and tested:
.. autosummary:... | {"hexsha": "5dc8cbe2201a76fc5d5b1782705a39499815b59d", "size": 49735, "ext": "py", "lang": "Python", "max_stars_repo_path": "pastas/stressmodels.py", "max_stars_repo_name": "RDWimmers/pastas", "max_stars_repo_head_hexsha": "999a7b6475ff5dfc023ab4a10512443196ec187b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import logging
import numpy as np
from .futils.futils import futils
logger = logging.getLogger(name="phd.core")
period = np.inf
def set_period(p):
"""Set the periodicity of periodic variables."""
global period
assert p > 0.0, "Period must be larger than 0 (gave %.3f)." % (p)
period = p
futils.s... | {"hexsha": "f5ff0b351c0040b602609ac7db8e094e95fa470e", "size": 2675, "ext": "py", "lang": "Python", "max_stars_repo_path": "phd/core.py", "max_stars_repo_name": "jusjusjus/phase-dynamics", "max_stars_repo_head_hexsha": "34af63c9d7514fe54cc9ef25ff01d3694f318923", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
#! /usr/bin/env python3
# Pulls up some image files and predicts ellipses & ring-counts for them.
# Does not perform any kind of scoring or evaluation. The assumption is
# that annotations for these images my not exist.
# disable FutureWarnings from numpy re. tensorflow
import warnings
with warnings.catch_warnings... | {"hexsha": "04a197a86ca48d91714209f6b619d2c8835cc35e", "size": 4788, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict_spnet.py", "max_stars_repo_name": "drscotthawley/SPNet", "max_stars_repo_head_hexsha": "94f1195c91e2373bee1f36bc7d834c4e07388369", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
% book : Signals and Systems Laboratory with MATLAB
% authors : Alex Palamides & Anastasia Veloni
%
%
%
% problem 6 - convolution of x(t) and h(t)
t1=0:.1:2;
t2=2.1:.1:4;
t3=4.1:.1:10;
x1=t1;
x2=4-t2;
x3=zeros(size(t3));
x=[x1 x2 x3];
t=0:.1:10;
h=t.*exp(-t);
y=conv(x,h)*0.1;
plot(0:.1:20,y);
title('System res... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/28762-signals-and-systems-laboratory-with-ma... |
import numpy as np
from . import units
import re
#import openbabel as ob
# => XYZ File Utility <= #
def read_xyz(
filename,
scale=1.):
""" Read xyz file
Params:
filename (str) - name of xyz file to read
Returns:
geom ((natoms,4) np.ndarray) - system geometry (atom symbol, x,y,... | {"hexsha": "5834b566efe39520ca729a2c36b45e6a1a001e75", "size": 5479, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygsm/utilities/manage_xyz.py", "max_stars_repo_name": "espottesmith/pyGSM", "max_stars_repo_head_hexsha": "5bf263f9ef6cbee3ec16355c5eb1839446e704e7", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import os
import sys
from time import time, strftime
from datetime import date
import components.processing.args_processing as arg_process
import components.grading.args_grading as arg_grading
import components.utilities.listbox as listbox
from components.processing.voi_extraction_pipelines import ... | {"hexsha": "92a5471148e93ee160dc09df9185c89b2347e687", "size": 3714, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/scripts/run_full.py", "max_stars_repo_name": "MIPT-Oulu/3D-Histo-Grading", "max_stars_repo_head_hexsha": "b779a154d0e5b104fc152c8952124768fb7b1dc6", "max_stars_repo_licenses": ["MIT"], "m... |
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import Divider, Size
import pandas as pd
from scipy.optimize import curve_fit
from scipy.stats import linregress
# This script plots the in vitro data taken for purified Scarlet-His.
# Data were analyzed with SPT (... | {"hexsha": "d1152dddd0343cb8be01348b7b0baf66c29cfd30", "size": 2597, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/fig1_Scarlet_pH_sensitivity/in_vitro_PBS_35C/plot_PBS_35C.py", "max_stars_repo_name": "jlazzaridean/mScarlet_lifetime_reports_pH", "max_stars_repo_head_hexsha": "13b022b1dc1fff8ebd0a881248... |
# Mathematical & Numerical functions
############################################
export locate,
interp,
deriv,
integ,
cuminteg,
smooth!,
smooth,
smooth_spline,
smooth_plaw,
gauss_laguerre_nw,
gauss_legendre_nw,
expi
######################... | {"hexsha": "0cc174744b418050206beac526b9e20006b8d1ff", "size": 17115, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/funcs.jl", "max_stars_repo_name": "natj/toolbox", "max_stars_repo_head_hexsha": "ca6a5f7d5cc37d673316b36117098a7feb648f51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars... |
infile = {{i.infile | quote}}
outfile = {{o.outfile | quote}}
obsvfile = {{o.obsvfile | quote}}
exptfile = {{o.exptfile | quote}}
{% if args.intype == 'cont' %}
#
# | Disease | Healthy |
# --------+---------+---------+
# mut | 40 | 12 |
# non-mut | 23 | 98 |
# --------+---------... | {"hexsha": "cc3041f879dd7fe1d96b5d67372dace6dd4f3ee6", "size": 2760, "ext": "r", "lang": "R", "max_stars_repo_path": "bioprocs/scripts/stats/pChiSquare.r", "max_stars_repo_name": "LeaveYeah/bioprocs", "max_stars_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import shutil
import torch
import numpy as np
from PIL import Image
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
def save_output_images(predictions, filenames, output_dir):
""... | {"hexsha": "b143b1a9c2a8f77a32871602e7118be5dadf0835", "size": 1309, "ext": "py", "lang": "Python", "max_stars_repo_path": "drn/io_util.py", "max_stars_repo_name": "janapavlasek/drn", "max_stars_repo_head_hexsha": "916314006c352da13e5ed0315f40aac2a4de0166", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
(***********************************************************************)
(* v * The Coq Proof Assistant / The Coq Development Team *)
(* <O___,, * INRIA-Rocquencourt & LRI-CNRS-Orsay *)
(* \VV/ *************************************************************)
(* // * This f... | {"author": "JasonGross", "repo": "category-coq-experience-tests", "sha": "f9949ede618788fd051fe8327f997ee683388e49", "save_path": "github-repos/coq/JasonGross-category-coq-experience-tests", "path": "github-repos/coq/JasonGross-category-coq-experience-tests/category-coq-experience-tests-f9949ede618788fd051fe8327f997ee6... |
#!/usr/bin/python
__author__ = ('David Dunn')
__version__ = '0.3'
import numpy as np
import cv2
import time
BACKEND = {'cv2':0, 'flyCap':1, 'picamera':2, 'spinnaker':3} # different camera APIs that are supported
try:
from shared_modules.pyfly2 import pyfly2
except ImportError:
print("Warning: FlyCapture ba... | {"hexsha": "15635100d983a01d022fc13bfe463d8989c961be", "size": 22766, "ext": "py", "lang": "Python", "max_stars_repo_path": "__init__.py", "max_stars_repo_name": "qenops/dCamera", "max_stars_repo_head_hexsha": "5bd8443a9a8d4997859554b5b4440cee7d65db0b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
import numpy as np
import random
from basenet.vision import transforms as btransforms
from torchvision import transforms
from PIL import Image, ImageEnhance, ImageOps
class Policy(object):
def __init__(self, params, fillcolor=(128, 128, 128), image_size=32):
"""
Get parameters from tuner to initi... | {"hexsha": "f30e8b676f264d2340c70a77e4ba4510d46e77c3", "size": 6264, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/nni_data_augmentation/augment.py", "max_stars_repo_name": "petuum/tuun", "max_stars_repo_head_hexsha": "8eec472dbf0e5e695449b0fa2d98985469fd5b30", "max_stars_repo_licenses": ["Apache-2.0"... |
"""
In this example, nmpc (Nonlinear model predictive control) is applied on a simple 2-dofs arm model. The goal is to
perform a rotation of the arm in a quasi-cyclic manner. The sliding window across iterations is advanced for a full
cycle at a time while optimizing three cycles at a time (main difference between cycl... | {"hexsha": "070cd16ca06daeca2fc08272e5a18c72e068ca62", "size": 4198, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/moving_horizon_estimation/multi_cyclic_nmpc.py", "max_stars_repo_name": "fbailly/BiorbdOptim", "max_stars_repo_head_hexsha": "3a5473ee7c39d645d960611596a45b044e8ccf58", "max_stars_repo_li... |
"""
histmag(mags, binwidth)
Group the magnitude values `mags` into magnitude bins of width `binwidth`.
Return an array containing the center of each bin and an array containing the
number of events in each bin. The left edge of the first bin corresponds to the minimum magnitude
in `mags`.
"""
function histmag(mags:... | {"hexsha": "709cd676bc4d8723f46608313867fa064a147414", "size": 608, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/binning.jl", "max_stars_repo_name": "riccmin/MagnitudeDistributions.jl", "max_stars_repo_head_hexsha": "3600a7ac9b6b7e2b0764830c333a388a18d73499", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma (in group) compl_fam_cong:
assumes "compl_fam (f \<circ> g) A" "inj_on g A"
shows "compl_fam f (g ` A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. compl_fam f (g ` A)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. compl_fam f (g ` A)
[PROOF STEP]
have "((f \<circ> g)... | {"llama_tokens": 1008, "file": "Finitely_Generated_Abelian_Groups_IDirProds", "length": 10} |
theory PermEnvEq
imports PermEnv
begin
(*
####################################
P1. main equality lemmas
####################################
*)
(* - add / rem lemmas *)
lemma ignore_add_use_env: "\<lbrakk> r = r_s x \<rbrakk> \<Longrightarrow> r_s = add_use_env r_s ... | {"author": "anon-ef", "repo": "perm_lang_ef2", "sha": "0fcb6e4c175193cc7b94f297a8aaa605f502d711", "save_path": "github-repos/isabelle/anon-ef-perm_lang_ef2", "path": "github-repos/isabelle/anon-ef-perm_lang_ef2/perm_lang_ef2-0fcb6e4c175193cc7b94f297a8aaa605f502d711/perm_ref/PermEnvEq.thy"} |
import librosa
import os
from os.path import join
import tensorflow as tf
from tqdm import tqdm
import numpy as np
from scipy.io import wavfile
BASE_DIR = '.'
AUDIO_DIR = join(BASE_DIR, 'submit')
OUTPUT_DIR = join(BASE_DIR, 'wav')
IN_SR = 22050
OUT_SR = 16000
MAX_BIT = 32767 # for 16bit bitrate
def downsample_libro... | {"hexsha": "53f5477e20345b723fa0f620cb024c05896e2004", "size": 1103, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/downsample.py", "max_stars_repo_name": "freds0/MOSNet", "max_stars_repo_head_hexsha": "2f6f32090b971337d3c56868e1b3b9f8a3317381", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 174, "... |
"""
backprop
~~~~~~~~
A module to implement the stochastic gradient descent learning
algorithm for a neural network. Gradients are calculated using
backpropagation. Note that I have focused on making the code simple,
easily readable, and easily modifiable. It is not optimized, and
omits many desirable features.
"""... | {"hexsha": "5a2de408d5c124cd9b217360960424d1597a66a7", "size": 7583, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/backprop.py", "max_stars_repo_name": "Alphatortoise/Neural-network-and-deep-learning", "max_stars_repo_head_hexsha": "73a3f682dad85e32981c8b1fc79b4d7267111f67", "max_stars_repo_licenses": ["U... |
from collections import defaultdict, Counter
from itertools import product, permutations
from glob import glob
import json
import os
from pathlib import Path
import pickle
import sqlite3
import string
import sys
import time
import matplotlib as mpl
from matplotlib import colors
from matplotlib import pyplot as plt
fro... | {"hexsha": "3276b79a61cf27161c545de376944d5851538c10", "size": 52691, "ext": "py", "lang": "Python", "max_stars_repo_path": "Src/si_figs.py", "max_stars_repo_name": "jomimc/FoldAsymCode", "max_stars_repo_head_hexsha": "1896e5768e738bb5d1921a3f4c8eaf7f66c06be9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
#ifndef BOOST_THREAD_WIN32_SHARED_MUTEX_HPP_MODIFIED
#define BOOST_THREAD_WIN32_SHARED_MUTEX_HPP_MODIFIED
// (C) Copyright 2006-8 Anthony Williams
//
// 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)
... | {"hexsha": "ab0f9f1b3f94fa178c06090af7ee369b28cb3ce8", "size": 20430, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/mongo/util/concurrency/shared_mutex_win.hpp", "max_stars_repo_name": "leifwalsh/mongo", "max_stars_repo_head_hexsha": "4cf51324255f76a110246f6d1646dc8cda570141", "max_stars_repo_licenses": ["Ap... |
[STATEMENT]
lemma document_ptr_kinds_M_eq:
assumes "|h \<turnstile> object_ptr_kinds_M|\<^sub>r = |h' \<turnstile> object_ptr_kinds_M|\<^sub>r"
shows "|h \<turnstile> document_ptr_kinds_M|\<^sub>r = |h' \<turnstile> document_ptr_kinds_M|\<^sub>r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. |h \<turnstile> doc... | {"llama_tokens": 359, "file": "Core_SC_DOM_common_monads_DocumentMonad", "length": 2} |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
def trend_dampen(damp_fact, trend):
zeroed_trend = trend - trend[0]
damp_fact = 1 - damp_fact
if damp_fact < 0:
damp_fact = 0
if damp_fact > 1:
damp_fact = 1
if damp_fact == 1:
dampened_trend = zeroed_trend
... | {"hexsha": "cec7dd9177878176c23f2daf93ee311820d3d86f", "size": 870, "ext": "py", "lang": "Python", "max_stars_repo_path": "ThymeBoost/utils/trend_dampen.py", "max_stars_repo_name": "scumechanics/ThymeBoost", "max_stars_repo_head_hexsha": "918cc446a9ca3da5e6ca989d79bd81484657f54a", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 9 13:20:53 2014
@author: ivan
"""
import random
import numpy as np
%precision 3
n = 1000000
x = [random.random() for _ in range(n)]
y = [random.random() for _ in range(n)]
x[:3], y[:3]
z = [x[i] + y[i] for i in range(n)]
z[:3]
%timeit [x[i] + y[i] for i in range(n)]... | {"hexsha": "93ff514b45bab1c745974b2ca168badbe317fbb7", "size": 697, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ipython_tutorial2.py", "max_stars_repo_name": "ivanliu1989/Afr-Soil-Prediction", "max_stars_repo_head_hexsha": "ccd327b12f2cd47e7f0a78302e86d5f8c2f25e78", "max_stars_repo_licenses": ["MIT"],... |
import scipy.io
import numpy as np
import spacy
from spacy.lang.en import English
def load_subj_dict(filepath: str) -> dict:
"""
Read one .mat file with the raw data for a single subject as provided by
Wehbe et al. (2014) into a Python dictionary format.
Description of the original Wehbe data:
... | {"hexsha": "9b4020a72658ae598f36ccef748a6c9153e88f58", "size": 13332, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/io.py", "max_stars_repo_name": "mdhk/nlrse-brainnlp", "max_stars_repo_head_hexsha": "90a89074166e033f82800eab602414fb8dbd5386", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
[STATEMENT]
lemma set_eqD1:
fixes R (structure)
assumes "A {.=} A'" and "a \<in> A"
shows "\<exists>a'\<in>A'. a .= a'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>a'\<in>A'. a .= a'
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
A {.=} A'
a \<in> A
goal (1 subgoal):
1. \<exists... | {"llama_tokens": 183, "file": null, "length": 2} |
import copy
import random
from collections import defaultdict
import numpy as np
import torch
from scipy.sparse import issparse
from torch.utils.data import Dataset
class MetalDataset(Dataset):
"""A dataset that group each item in X with its label from Y
Args:
X: an n-dim iterable of items
Y... | {"hexsha": "9cebef9040b8e88598c156069b379f68793d82d2", "size": 11606, "ext": "py", "lang": "Python", "max_stars_repo_path": "metal/utils.py", "max_stars_repo_name": "dliangsta/metal", "max_stars_repo_head_hexsha": "49c568e33b36f5e0887bd977dca936b7def02ad7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
[STATEMENT]
lemma find_closest_code_eq:
assumes "0 < length ps" "\<delta> = dist c\<^sub>0 c\<^sub>1" "\<delta>' = dist_code c\<^sub>0 c\<^sub>1" "sorted_snd (p # ps)"
assumes "c = find_closest p \<delta> ps" "(\<delta>\<^sub>c', c') = find_closest_code p \<delta>' ps"
shows "c = c'"
[PROOF STATE]
proof (prove)
g... | {"llama_tokens": 13404, "file": "Closest_Pair_Points_Closest_Pair", "length": 47} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 16 15:59:45 2020
@author: elijahsheridan
"""
import numpy as np
import opt_helper as opt
import scipy.optimize as op
#import matplotlib.pyplot as plt
def exp(x, p0, p1): return np.exp(p0 + p1 * x)
def poly(x, p0, p1, p2, p3, p4): return p0 + p1 *... | {"hexsha": "16a5c853f280d4efb251d44698af70e02fdb71d4", "size": 4086, "ext": "py", "lang": "Python", "max_stars_repo_path": "post_optimization_studies/curve_fitting.py", "max_stars_repo_name": "sheride/axion_pheno", "max_stars_repo_head_hexsha": "7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5", "max_stars_repo_licenses": ["MI... |
# -*- coding: utf-8 -*-
import copy
import itertools
import types
import cv2
import numpy as np
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from sldc import DefaultTileBuilder, Image, TileTopologyIterator
from cell_counting.base_method import BaseMethod
from cell_coun... | {"hexsha": "36e7813853f0f2b912cf5b3355426c7fbb1e41f9", "size": 7361, "ext": "py", "lang": "Python", "max_stars_repo_path": "cytomine-datamining/algorithms/counting/cell_counting/cnn_methods.py", "max_stars_repo_name": "Cytomine-ULiege/Cytomine-python-datamining", "max_stars_repo_head_hexsha": "16db995f175e8972b8731a8df... |
from __future__ import division
import math
import matplotlib as mpl
import numpy as np
from matplotlib.ticker import AutoMinorLocator
from matplotlib.ticker import MultipleLocator
from matplotlib.ticker import FixedLocator
from matplotlib.ticker import LogLocator
from matplotlib.ticker import FormatStrFormatter
fr... | {"hexsha": "33d1a5652698c6e335759b5d60f8b9e1a3093bed", "size": 7245, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/qm.py", "max_stars_repo_name": "tripatheea/Riemann-Zeta", "max_stars_repo_head_hexsha": "661b74526786d9395f986e2baf61b03a2de34040", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
from flask.helpers import make_response
from flask.json import jsonify
import numpy as np
import pandas as pd
from flask import Flask, request, jsonify, render_template
from tensorflow import keras
import tensorflow_decision_forests as tfdf
import firebase_admin
from firebase_admin import credentials
from firebase_admi... | {"hexsha": "b26e948156d1c2a29e615331b81e429edfab2ab1", "size": 16897, "ext": "py", "lang": "Python", "max_stars_repo_path": "HipotesaCloudManagement/main_backup.py", "max_stars_repo_name": "davindb/Symptoms-Based-Disease-Prediction", "max_stars_repo_head_hexsha": "0dbce12e7ee7b9ada411881b8e087a079446a5e9", "max_stars_r... |
#include "alkinectmanager.hpp"
#include <boost/thread/thread.hpp>
#include <iostream>
int main(int, char **) {
AlKinectManager m;
m.init();
while (1) {
// std::cout << "oper" << std::endl;
boost::this_thread::sleep_for(boost::chrono::milliseconds(33));
}
return 0;
}
| {"hexsha": "0850984e27e4a4d9d4fa72e9e7260bb26bf11e19", "size": 303, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "al_kinect_win/src/main.cpp", "max_stars_repo_name": "xorsnn/altexo", "max_stars_repo_head_hexsha": "2587ecd66a970d6805cc40635a9ec19786b05dce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
# encoding: UTF-8
import numpy
class LRFData:
"""
LRFData contains Linear RF data.
Properties:
n: Number of points
p: Position (1D)
f: Field (1D)
df: Differential of Field (1D)
"""
def __init__(self, p, f, df, copy=True):
"""
Initialize LRFData ob... | {"hexsha": "a903da01f2586faa1b7c4952a3fa4cac295dda10", "size": 4859, "ext": "py", "lang": "Python", "max_stars_repo_path": "phyutil/phylib/fieldmap/impact/lrfdata.py", "max_stars_repo_name": "frib-high-level-controls/phyhlc", "max_stars_repo_head_hexsha": "6486607e3aa0212054a12e9f2ad1a3ef15542f48", "max_stars_repo_lice... |
import glob
from os import path
import pandas as pd
import numpy as np
from ..telescope import Telescope
from datetime import timedelta
from astropy.io import fits
from tqdm import tqdm
from astropy.time import Time
import os
import zipfile
def phot2dict(filename):
hdu = fits.open(filename)
dictionary = {h.na... | {"hexsha": "03d877e4794796faa714b5b0bfc344d9f17a28cd", "size": 4792, "ext": "py", "lang": "Python", "max_stars_repo_path": "prose/io/io.py", "max_stars_repo_name": "lgrcia/prose", "max_stars_repo_head_hexsha": "bf5482f775eb8cfee261620901cebafb6edb650a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "max_st... |
import numpy as np
import effects.base
import stretch
class VfFlag(effects.base.WorldEffectBase):
@staticmethod
def apply(params) -> np.ndarray:
'''
| 疑似エッジ
| vfフラグでエッジがかかる長さを5ms単位で指定します。
| vfフラグが正の場合冒頭から、負の場合固定範囲の末尾からです。
| vfwフラグは、エッジ1回の長さを1000フレームに対する割合で指定します。
... | {"hexsha": "e0b8167eb18a6960874a7a2b134d60497f75ec13", "size": 2191, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyRwu/effects/vf_flag.py", "max_stars_repo_name": "delta-kimigatame/PyRwu", "max_stars_repo_head_hexsha": "6ea00d8d6a61de34d389a741b1b656f0987da998", "max_stars_repo_licenses": ["MIT"], "max_stars... |
(*
Copyright 2014 Cornell University
Copyright 2015 Cornell University
This file is part of VPrl (the Verified Nuprl project).
VPrl is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 o... | {"author": "coq-contribs", "repo": "intuitionistic-nuprl", "sha": "6279ed83244dc4aec2e23ffb4c87e3f10a50326d", "save_path": "github-repos/coq/coq-contribs-intuitionistic-nuprl", "path": "github-repos/coq/coq-contribs-intuitionistic-nuprl/intuitionistic-nuprl-6279ed83244dc4aec2e23ffb4c87e3f10a50326d/rules_apply_callbyval... |
import os
import json
import pickle
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.mixture import BayesianGaussianMixture
class FisherVectorGMM:
"""
Fisher Vector derived from GMM
---
Attributes
-----------
n_kernels: int
number of kernels in GMM
convar... | {"hexsha": "18b2563570c2a1ab1d0b4588a7fcf38d9ae805fd", "size": 10615, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model/fisher_encoder.py", "max_stars_repo_name": "ZihengZZH/bipolar-disorder", "max_stars_repo_head_hexsha": "deef966d65014175d6cb8f35320b2b33bfadfd13", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python
"""
# Generate line-of-sight interferograms
#
# input: disclocOutput
# output: image
#
# usage:
# python SARImage.py (testing with default data set and parameters)
# python SARIMage.py dislocOutput imageURL
# python SARImage.py dislocOutput elevation(degree) azimuth(degree) radarFrequency(i... | {"hexsha": "2709fb98547cfedb4d24bf490685e64832c2b05d", "size": 11032, "ext": "py", "lang": "Python", "max_stars_repo_path": "kml_plotting_scripts/SARImage.py", "max_stars_repo_name": "GeoGateway/geogateway-portal", "max_stars_repo_head_hexsha": "9276f35a4011e6361f08b2a1e7fc1e871f532f74", "max_stars_repo_licenses": ["Ap... |
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
def train_cnn(train_datagen, training_images, training_labels, validation_datagen, testing_images, testing_labels):
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(64, (3, 3), acti... | {"hexsha": "e71835857d5efded03369f4232c595196bfe014a", "size": 1391, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/exploration/CNN.py", "max_stars_repo_name": "rogov-dvp/medical-imaging-matching", "max_stars_repo_head_hexsha": "ab5dd4c4f4422c170adb2a3228fcb88fb2a5ffe4", "max_stars_repo_licenses": ["MIT"],... |
"""
Copyright (C) 2022 Martin Ahrnbom
Released under MIT License. See the file LICENSE for details.
This module describes the Kalman filters used by GUTS/UTS
"""
import numpy as np
from filterpy.kalman import UnscentedKalmanFilter, JulierSigmaPoints
from filterpy.kalman import ExtendedKalmanFilter
from... | {"hexsha": "8b96b12fc855995123a719c5be31f0177bccb5c3", "size": 6806, "ext": "py", "lang": "Python", "max_stars_repo_path": "filter.py", "max_stars_repo_name": "ahrnbom/guts", "max_stars_repo_head_hexsha": "9134e7f6568a24b435841e5934a640bdbe329a68", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
## @ingroup Analyses-Atmospheric
# Constant_Temperature.py
#
# Created: Mar 2014, SUAVE Team
# Modified: Feb 2016, A. Wendorff
# Jan 2018, W. Maier
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------------------------------------
imp... | {"hexsha": "a03f457064cff48d058543aa284d11d36d576088", "size": 5749, "ext": "py", "lang": "Python", "max_stars_repo_path": "SUAVE/SUAVE-2.5.0/trunk/SUAVE/Analyses/Atmospheric/Constant_Temperature.py", "max_stars_repo_name": "Vinicius-Tanigawa/Undergraduate-Research-Project", "max_stars_repo_head_hexsha": "e92372f078824... |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown Copyright 2017-2020 Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | {"hexsha": "4014cbfad03ecf7b431200d7c11407def662770f", "size": 39482, "ext": "py", "lang": "Python", "max_stars_repo_path": "improver_tests/nowcasting/lightning/test_NowcastLightning.py", "max_stars_repo_name": "pnijhara/improver", "max_stars_repo_head_hexsha": "5961a6fab9a79cd63a943eff07bf79d4e5f0ff03", "max_stars_rep... |
import numpy as np
import constants as c
import pyrosim.pyrosim as pyrosim
import pybullet as p
class Sensor():
def __init__(self, linkName):
self.linkName = linkName
self.values = np.zeros(c.simulation_length)
def GetValue(self, step):
self.values[step] = pyrosim.Get_Touch_Sensor_Value... | {"hexsha": "687de0c12146489b3eed2ee8f7c5891c52d3f7e9", "size": 1182, "ext": "py", "lang": "Python", "max_stars_repo_path": "sensor.py", "max_stars_repo_name": "jacksonsdean/evolutionary-robotics-", "max_stars_repo_head_hexsha": "af333afb03dcb3759da233aecd93975dde17df7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#! /usr/bin/python
# -*- encoding: utf-8 -*-
from __future__ import print_function, unicode_literals
import numpy as np
import theano.tensor as T
from theano import shared, config, function
__author__ = 'fyabc'
fX = config.floatX
def toFX(value):
return eval('%s(value)' % fX)
class PolicyNetwork(object):
... | {"hexsha": "82df26f012a68dfa663c12fbe4001506ccba0758", "size": 1429, "ext": "py", "lang": "Python", "max_stars_repo_path": "FeiTianProject/start.py", "max_stars_repo_name": "fyabc/MSRAPaperProject", "max_stars_repo_head_hexsha": "2d7974acfe8065523d0c56da695807e94acd0b34", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Illustrate imputation of an N*D partially observed data matrix by fitting a Gaussian using EM and then predicting missing entries
# authors: Drishttii@, murphyk@
import pyprobml_utils as pml
import gauss_utils as gauss
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_spd_matrix
f... | {"hexsha": "49bba4c0ada724f055282c9524cda9cefc8d2908", "size": 2249, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/gauss_imputation_em_demo.py", "max_stars_repo_name": "karalleyna/pyprobml", "max_stars_repo_head_hexsha": "72195e46fdffc4418910e76d02e3d6469f4ce272", "max_stars_repo_licenses": ["MIT"], "m... |
using Zygote, LinearAlgebra
using ArnoldiMethod: partialschur, SR
function test_eigmin(A::AbstractMatrix)
decomp, history = partialschur(A; nev = 1, which = SR())
ev = minimum(real.(decomp.eigenvalues))
return ev
end
A = rand(4, 4); A = A + A';
@show eigmin(A)
@show test_eigmin(A)
@show Zygote.gradient(t... | {"hexsha": "ded4c0acc4adf46c10d3021248d4af5e7391d304", "size": 335, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Methods/ArnoldiMethod.jl", "max_stars_repo_name": "ericphanson/Attempt-diff-eigmin", "max_stars_repo_head_hexsha": "e98806ed7f164b68fc9c270fc84f56a77ca994a3", "max_stars_repo_licenses": ["MIT"], "ma... |
# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
import funsor.ops as ops
from funsor.cnf import BACKEND_TO_EINSUM_BACKEND, BACKEND_TO_LOGSUMEXP_BACKEND
from funsor.einsum import einsum, naive_plated_einsum
from funsor.interpretations import memoize
... | {"hexsha": "c3330adb38dd0e07fd403b6e82b8d9cccc1bcf94", "size": 6496, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_memoize.py", "max_stars_repo_name": "fritzo/funsor", "max_stars_repo_head_hexsha": "1d07af18c21894dd56e2f4f877c7845430c3b729", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
"""
Here 2 version for peakdetector.
"""
import numpy as np
#~ from pyacq.core.stream.ringbuffer import RingBuffer
from .tools import FifoBuffer
try:
import pyopencl
mf = pyopencl.mem_flags
HAVE_PYOPENCL = True
except ImportError:
HAVE_PYOPENCL = False
def detect_peaks_in_chunk(sig, k, thresh, pe... | {"hexsha": "7fc65bbd5d8a7cd94a0b334f1feb443e6a24a91b", "size": 10101, "ext": "py", "lang": "Python", "max_stars_repo_path": "tridesclous/peakdetector.py", "max_stars_repo_name": "rdarie/tridesclous", "max_stars_repo_head_hexsha": "178c0a67d7b3ac88be8e4383001396c1e0f976c2", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import cv2
ROOT_COLAB = '.'
YOLO_CONFIG = ROOT_COLAB + '/yolo_env/'
COCO_LABELS_FILE = YOLO_CONFIG + 'piford.names'
YOLO_CONFIG_FILE = YOLO_CONFIG + 'yolov4-custom.cfg'
YOLO_WEIGHTS_FILE = YOLO_CONFIG + 'yolov4-custom_best.weights'
IMAGE_FILE = 'img/Dataset/frame23.jpg'
IMAGE = cv2.imread(ROOT_COLAB... | {"hexsha": "5be65e81dd9ce8e819b482a7d168f965f7a88b73", "size": 2826, "ext": "py", "lang": "Python", "max_stars_repo_path": "pypoke.py", "max_stars_repo_name": "Elkantar-git/pogo", "max_stars_repo_head_hexsha": "64ff9a3d1d9192e7695eacae556be2b1bf6dfcd7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_sta... |
#free fall model
#joshlucpoll.com
'''
using the equation:
a = (W - D) / m
Ref:
https://www.grc.nasa.gov/www/k-12/airplane/falling.html
'''
import inputs
from texttable import Texttable
import webbrowser
import matplotlib.pyplot as plt
import numpy as np
def calculate(timeInterval, acceleration, initialVelocity, d... | {"hexsha": "e2277a503ee322e6088332ebc80b2cc4ffcdb163", "size": 3953, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "Joshlucpoll/freefallModelCalcualtor", "max_stars_repo_head_hexsha": "32219080f34dbac28728cc0d6925a771f38b0e59", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import argparse
import torch
import utils
import os
import pickle
import matplotlib.pyplot as plt
from torch.utils import data
import numpy as np
from collections import defaultdict
import modules
torch.backends.cudnn.deterministic = True
parser = argparse.ArgumentParser()
parser.add_argument('--save-folder', type... | {"hexsha": "be03dc63c614f6be6eb0da792b46692681a20c12", "size": 6189, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_vis.py", "max_stars_repo_name": "dmcinerney/c-swm", "max_stars_repo_head_hexsha": "8de4a38a906de0f2f53b6e937b7b4a0b6b6a545a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import numpy as np
from bandstructure import Parameters
from bandstructure.system import TightBindingSystem
from bandstructure.lattice import RegularChain
def test_paramter_change(recwarn):
lattice = RegularChain()
params = Parameters({
'lattice': lattice,
't': 1
})
s = TightBinding... | {"hexsha": "20e633893938d4750833c21c04e35cd0dcc94bc5", "size": 867, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_system.py", "max_stars_repo_name": "sharkdp/bandstructure", "max_stars_repo_head_hexsha": "b74b688afc2b15b20ec1a8ebcf72ba8699b6bf96", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from sklearn.cluster import KMeans
import numpy as np
import logging
import sys
import os
import warnings
from sklearn import metrics
from sklearn.metrics.cluster import normalized_mutual_info_score
from sklearn.metrics.cluster import adjusted_mutual_info_score
if not sys.warnoptions:
warnings.simplefilter("ignore... | {"hexsha": "a4929bebf594a2f25445f8ca41ad913d7881e21b", "size": 1250, "ext": "py", "lang": "Python", "max_stars_repo_path": "cluster.py", "max_stars_repo_name": "yusonghust/RGAE", "max_stars_repo_head_hexsha": "0b8c6ff5039a91c74528dd5ad3081e673126e1d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
from scattering import *
mesh_file = "mesh/hexa.msh"
permittivity_dict = {1: 1, 2: 11.8, 3: 1}
s = np.array([1, 2])
p = np.array([-2, 1])
k0L = np.pi
problem = IsotropicScattering(mesh_file, permittivity_dict, k0L)
pw = PlaneWave(s, p)
E = problem.solve(pw)
#phi, FF = problem.get_far_field(E, 40)... | {"hexsha": "127c4a01c817495785a3a82888c2b7e5838d4bd9", "size": 359, "ext": "py", "lang": "Python", "max_stars_repo_path": "isotropic_test.py", "max_stars_repo_name": "dbojanjac/scattering2D", "max_stars_repo_head_hexsha": "f82f38cceea362ad0a40c23de31264ab1b27d01f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, me... | {"hexsha": "b4d26d046237d83c1178b282c0e54fa06611752d", "size": 3770, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/inter_center_loss_testPy.py", "max_stars_repo_name": "hengxyz/facenet_class_wise_triplet_loss_pub", "max_stars_repo_head_hexsha": "ba93e3016006616310decd7e55d554d06ccb0c22", "max_stars_repo_l... |
# /*
# ////////////////////////////////////////////////////////////////////////
# * Luiz Felipe Raveduti Zafiro - RA: 120513
# * Artificial Intelligence - Federal University of São Paulo (SJC)
# * Nayve Bayes Algorithm for IRIS DataSet
# ////////////////////////////////////////////////////////////////////////
# */
im... | {"hexsha": "a2532f4631dedc54554be68764962998c4977696", "size": 8052, "ext": "py", "lang": "Python", "max_stars_repo_path": "IRIS-DataSet/nayve.py", "max_stars_repo_name": "LZafiro/Artificial-Intelligence-Practice", "max_stars_repo_head_hexsha": "60309d76437f6d21affa45fd357ea7c362a208cf", "max_stars_repo_licenses": ["MI... |
"""
test read spec
"""
import os
from gpy_dla_detection.read_spec import read_spec, retrieve_raw_spec
import numpy as np
def test_read_spec():
if not os.path.exists("spec-7340-56825-0576.fits"):
retrieve_raw_spec(7340, 56825, 576) # an arbitrary spectrum
wavelengths, flux, noise_variance, pixel_mask... | {"hexsha": "2279ae73fb8712a026b6b0f5fd1322ac0872c6a3", "size": 499, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_read_spec.py", "max_stars_repo_name": "jibanCat/gpy_dla_detection", "max_stars_repo_head_hexsha": "4d987adec75a417313fdc6601ee41a0ea60a0a2e", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Model
def plot_classification_history(history) -> None:
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.plot(
history.epoch, np.array(history.history['acc']), label='Training Accuracy'
)
plt.plot(
history.epoch, np.array... | {"hexsha": "e6f42a590a0e2adfda4b46224a90aaf4649a9668", "size": 1155, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/plotting/plot.py", "max_stars_repo_name": "robertvunabandi/mit-smart-confessions-data", "max_stars_repo_head_hexsha": "b2a447497af539c279587cda326b69a410cee4f3", "max_stars_repo_licenses": ... |
''' note these tests really need a GPU. XXX add a skip, or CPU versions of test. '''
import os
import time
import numpy as np
import py.test
import tensorflow as tf
from ggplib.db import lookup
from ggpzero.nn.manager import get_manager
from ggpzero.util import cppinterface
from ggpzero.defs import confs, templat... | {"hexsha": "352962724c87ac7304b47f743f463f2652134b11", "size": 6007, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/test/cpp/test_interface.py", "max_stars_repo_name": "ggplib/ggplearn", "max_stars_repo_head_hexsha": "52164bcd6f43d648736e1ae9e556a7f6412339d1", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import textwrap
import torch
from abc import ABC, abstractmethod
import ipdb as pdb
import torch
class DFA(object):
def __init__(self, sigma, Q, delta, q0, F):
self.sigma = sigma
self.Q = Q
self.delta = delta
self.q0 = q0
self.F = F
def __call__(self, string):
qt = self.q0
for symb... | {"hexsha": "1611097e2bd6dbccb82db61c0484ad7bcc977bbb", "size": 6705, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/crl_generator.py", "max_stars_repo_name": "satwik77/Transformer-Formal-Languages", "max_stars_repo_head_hexsha": "48eea2ea6e2802ba827868723f75fa6c82401cde", "max_stars_repo_licenses": ["... |
'''
Excited States software: qFit 3.0
Contributors: Saulo H. P. de Oliveira, Gydo van Zundert, and Henry van den Bedem.
Contact: vdbedem@stanford.edu
Copyright (C) 2009-2019 Stanford University
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation f... | {"hexsha": "1f92197aac7c1496149309237cc4df850a72cafc", "size": 10503, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/qfit/relabel.py", "max_stars_repo_name": "sakibh/qfit-3.0", "max_stars_repo_head_hexsha": "fcc9d56b21d2d16ffb2796da0d48003649a31909", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
module YaoLang
using LinearAlgebra
using YaoAPI
include("runtime/locations.jl")
include("runtime/generic_circuit.jl")
module Compiler
using TimerOutputs
const to = TimerOutput()
timings() = (TimerOutputs.print_timer(to); println())
enable_timings() = (TimerOutputs.enable_debug_timings(Compiler); return)
using Expr... | {"hexsha": "f08d80e33741ed7868e920523c1b5b8cebce7de4", "size": 1315, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/YaoLang.jl", "max_stars_repo_name": "NunoEdgarGFlowHub/YaoLang.jl", "max_stars_repo_head_hexsha": "a927aff5a08669737aee80c5b6a71a89dbae641f", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
from math import ceil
from sage.all import ZZ
from sage.all import sqrt
def factorize(N, rp, rq):
"""
Recovers the prime factors from a modulus using the Ghafar-Ariffin-Asbullah attack.
More information: Ghafar AHA. et al., "A New LSB Attack on Special-Structured RSA Primes"
:param N: the modulus
... | {"hexsha": "6c30c0a360c470e3cdc1de9d03df20a34dd9b7c2", "size": 964, "ext": "py", "lang": "Python", "max_stars_repo_path": "attacks/factorization/gaa.py", "max_stars_repo_name": "jvdsn/crypto-attacks", "max_stars_repo_head_hexsha": "df37f112c28687efd105b7770b1baa4c53a71ad8", "max_stars_repo_licenses": ["MIT"], "max_star... |
from json.tool import main
import os, sys
os.environ['HOME'] = '/disk/ocean/zheng/' # for server only
os.environ['MPLCONFIGDIR'] = "/disk/ocean/zheng/.config/matplotlib/" # for server only
from matplotlib import pyplot as plt
# matplotlib inline
import numpy as np
import pickle
import pandas
import gzip
import argparse... | {"hexsha": "bff9ac5e78d53e04a6ae25b76dee25f9676b0c19", "size": 8514, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis.py", "max_stars_repo_name": "zsquaredz/svcca", "max_stars_repo_head_hexsha": "296c64aa7e98c1222248004f14ce038dd467b5fd", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
import onnx
from onnx import numpy_helper
from onnxsim import simplify
class OnnxInterModel:
def __init__(self, onnx_path, use_simplify=False):
self._onnx_model = onnx.load(onnx_path)
if use_simplify:
self._onnx_model, check = simplify(self._onnx_model)
assert check, "Simpl... | {"hexsha": "56c70be91bf8861632b249695b35b49c9cbe07b0", "size": 4736, "ext": "py", "lang": "Python", "max_stars_repo_path": "handlers/onnx_graph_parser.py", "max_stars_repo_name": "Northengard/torch2tf_converter", "max_stars_repo_head_hexsha": "8d16c5364d97e25ed04c89704d9768b4bffc1c6b", "max_stars_repo_licenses": ["MIT"... |
module Rotations3D
using StaticArrays
using LinearAlgebra: norm, rank, svd, Diagonal, tr
using Combinatorics: permutations
export ClebschGordan, Rot3DCoeffs, ri_basis, rpi_basis, R3DC, Rot3DCoeffsEquiv
"""
`ClebschGordan: ` storing precomputed Clebsch-Gordan coefficients; see
`?clebschgordan` for the convention th... | {"hexsha": "66870ef29b78d9746aebbaa2785bc52a1bb44301", "size": 8960, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rotations3d.jl", "max_stars_repo_name": "cortner/ACE.jl", "max_stars_repo_head_hexsha": "8451214cc286104a4f4560f8042cccbc0c334c54", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": nul... |
module ElboMaximize
using Optim
using Optim: Options, NewtonTrustRegion
using ..Model
using ..SensitiveFloats
using ..DeterministicVI
using ..DeterministicVI: init_thread_pool!, ElboIntermediateVariables
using ..DeterministicVI.ConstraintTransforms: TransformDerivatives,
V... | {"hexsha": "d49d4f109dacdb0da0ab13107f4a8c514ea54e4c", "size": 9279, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/deterministic_vi/ElboMaximize.jl", "max_stars_repo_name": "giordano/Celeste.jl", "max_stars_repo_head_hexsha": "2353019d3a737129364b5fa88220e37be07eebea", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python3
from ...main.basic.read import RawDataImport, RetrospectDataImport, GetFiles
from ...toolbox.technical import emptyNumpyArray
from ...troubleshoot.err.error import *
import pandas
import numpy
import sys
import os
"""
Support emmer.bake Projection mode
Plot new observation onto the existing ... | {"hexsha": "14b0342c36d882c4e4228beb2b3bcaf4ab06331a", "size": 9291, "ext": "py", "lang": "Python", "max_stars_repo_path": "piemmer/posthoc/visual/projection.py", "max_stars_repo_name": "HWChang/emmer", "max_stars_repo_head_hexsha": "9d1ca071bd9f8d0e1ed49910de33a865d82df4c2", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
%---------------------------------------------------------------------------------
\chapter{Fitzhugh-Nagumo Model Example}
\label{chap:fitzhugh-nagumo}
%---------------------------------------------------------------------------------
\section{Background}
\label{sec:background}
We will look into Fitzhugh-Nagumo model a... | {"hexsha": "3d7daea99fbc26ebab7e873a508966be5143339f", "size": 6454, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/fitzhugh_nagumo.tex", "max_stars_repo_name": "FarmHJ/numerical-solver", "max_stars_repo_head_hexsha": "8a9b823b0ca6eb3c714c055324f35c74d5af5263", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
\section{Hierarchical Basis and Hierarchical Subspace}
\label{sec:22hierSubspaces}
\minitoc{67mm}{5}
\noindent
The dimension of the nodal space $\ns{\*l}$ is given by
\begin{equation}
\label{eq:dimensionFG}
\dim \ns{\*l}
= \setsize{\fgset{\*l}}
= \prod_{t=1}^d (2^{l_t} + 1).
\end{equation}
If we choose the sa... | {"hexsha": "950d42bd505e9987c6cf2c4f6964afb31c7d6450", "size": 11343, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/document/22hierSubspaces.tex", "max_stars_repo_name": "valentjn/thesis-arxiv", "max_stars_repo_head_hexsha": "ae30179e67cd6a7813385e140b609546fd65b897", "max_stars_repo_licenses": ["CC0-1.0"], ... |
[STATEMENT]
lemma SourcesA41_L1: "Sources level1 sA41 = {sA11, sA22, sA23, sA31, sA32, sA41}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Sources level1 sA41 = {sA11, sA22, sA23, sA31, sA32, sA41}
[PROOF STEP]
by (metis DSourcesA41_L1 SourcesA31_L1 SourcesA32_L1 Sources_2DSources Un_absorb Un_commute Un_insert_le... | {"llama_tokens": 178, "file": "ComponentDependencies_DataDependenciesCaseStudy", "length": 1} |
# -*- coding: utf-8 -*-
import unittest
import numpy as np
from ridge.models import NNMatFac
class TestNNMatFac(unittest.TestCase):
def setUp(self):
"""Set up this test suite.
Variables
---------
data : np.ndarray, whose shape is (n_users, n_items).
"""
self.data ... | {"hexsha": "2dad31b6bf11ea91e9993b68ef1abc1735a220e0", "size": 811, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_NMF.py", "max_stars_repo_name": "moriaki3193/ridge", "max_stars_repo_head_hexsha": "8fbcf1cd72d6b56786170f1ac4af3d8b8a9d1986", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "... |
import pytest
import numpy as np
from ArbitrageGraph import ArbitrageGraph
from OrderBook import OrderBookPair
from OrderRequest import OrderRequestType
class TestClass(object):
def test_intraExchange(self):
arbitrageGraph = ArbitrageGraph()
edgeTTL=5
arbitrageGraph.updatePoint(
... | {"hexsha": "070a24f49bd79020dbde6d1aefc154c9dd0e149b", "size": 13481, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ArbitrageGraph_test.py", "max_stars_repo_name": "gbarany/crypto-arbitrage-finder", "max_stars_repo_head_hexsha": "8b1dcf14cce795f4d0bbfa640abf3e13fe75eedb", "max_stars_repo_licenses": ["Apach... |
import pytest
import torch
import numpy as np
from src.utils import IoU, parametrize, unparametrize
def test_iou():
assert IoU([1, 1, 10, 10], [1, 1, 10, 10]) == 1.0
assert IoU([0, 0, 10, 10], [0, 0, 10, 9]) == 0.9
assert IoU([0, 0, 10, 10], [0, 0, 5, 5]) == 0.25
assert IoU([0, 0, 10, 10], [20, 20, 50,... | {"hexsha": "9e280f28e108dc448ec69f94295705cb5a3735b7", "size": 1581, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_utils.py", "max_stars_repo_name": "clemkoa/fasterrcnn", "max_stars_repo_head_hexsha": "bdd4d9bec141477f61f0ff8f53a19ae0924f2cc7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from line_search_methods import line_search_dict
from main_methods import main_method_dict
from config import simple_test_params
MAIN_METHOD_ORDER = {
'NewtonsMethod': 0,
'GradientDescentMethod': 1,
'ConjugateGradientMethod': 2,
'HeavyBallMethod': 3
}
FIGURE_TEMPLATE = """\\begin{{... | {"hexsha": "eefd0b0098678af981714ac8cd8d2fda33a2302b", "size": 7308, "ext": "py", "lang": "Python", "max_stars_repo_path": "export.py", "max_stars_repo_name": "EinariTuukkanen/line-search-comparison", "max_stars_repo_head_hexsha": "7daa38779017f26828caa31a53675c8223e6ab8e", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright 2016, 2017 California Institute of Technology
# Users must agree to abide by the restrictions listed in the
# file "LegalStuff.txt" in the PROPER library directory.
#
# PROPER developed at Jet Propulsion Laboratory/California Inst. Technology
# Original IDL version by John Krist
# Python transla... | {"hexsha": "c958877f1491c423425a942029c6771c8e5d3b62", "size": 6008, "ext": "py", "lang": "Python", "max_stars_repo_path": "Proper/proper/prop_lens.py", "max_stars_repo_name": "RupertDodkins/medis", "max_stars_repo_head_hexsha": "bdb1f00fb93506da2a1f251bc6780e70e97a16c5", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\subsection{True and False}
We start off with two statements:
\begin{itemize}
\item True - \(T\) or \(\top \)
\item False - \(F\) or \(\bot \)
\end{itemize}
| {"hexsha": "2a6a101d0c255429c2ab4daba15ed6b2309a4347", "size": 160, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/logic/propositionalLogic/01-01-truth.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_l... |
from __future__ import print_function
import os
import unittest
from shutil import rmtree
import numpy
import nifty
WITH_HDF5 = nifty.Configuration.WITH_HDF5
try:
import h5py
WITH_H5PY = True
except ImportError:
WITH_H5PY= False
class TestHDF5(unittest.TestCase):
tempFolder = './tmp_hdf5'
def ... | {"hexsha": "6a7be3bc196e6ee9848ac99d76273bdf0de299be", "size": 4606, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/test/hdf5/test_hdf5.py", "max_stars_repo_name": "konopczynski/nifty", "max_stars_repo_head_hexsha": "dc02ac60febaabfaf9b2ee5a854bb61436ebdc97", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma incidence_mat_non_empty_blocks:
assumes "j < \<b>"
shows "1 \<in>$ (col N j)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 1 \<in>$ col N j
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. 1 \<in>$ col N j
[PROOF STEP]
obtain bl where isbl: "\<B>s ! j = bl"
[PROOF STATE... | {"llama_tokens": 1772, "file": "Fishers_Inequality_Incidence_Matrices", "length": 23} |
C Copyright(C) 1999-2020 National Technology & Engineering Solutions
C of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with
C NTESS, the U.S. Government retains certain rights in this software.
C
C See packages/seacas/LICENSE for details
INTEGER FUNCTION GETPRC()
C returns the precis... | {"hexsha": "391e83e53083954847e3448deb46a630aa42c0a0", "size": 476, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/explore/exp_getprc.f", "max_stars_repo_name": "jschueller/seacas", "max_stars_repo_head_hexsha": "14c34ae08b757cba43a3a03ec0f129c8a168a9d3", "max_stars_repo_licenses": ... |
% Created 2018-06-03 Sun 07:47
% Intended LaTeX compiler: pdflatex
% Template: Diogo Ferrari
\documentclass[a4paper]{article}
% === Packages =================================
\usepackage{./sty/basic-article}
\usepackage{./sty/math-commands}
\usepackage{./sty/math-commands-thm}
% === Document ===========================... | {"hexsha": "3217370756beb54e2e5d3b27a336a760289e42c9", "size": 36178, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "vignettes/edar.tex", "max_stars_repo_name": "PabloSerrati/edar", "max_stars_repo_head_hexsha": "0d373ed0fae091fd30b001546560c788f5cd59cf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/env python3
import sys
import numpy
import csv
#neurons
exc_neuron_num=int(sys.argv[1])
#soma exc synapse
EEmax=0.3 #RS:0.3 IB:0.2
EEwidth=5.0 #5.0 is min
AMPA_NMDA_ratio=0.2
#E<-E
f=open("WEEinit.csv","w")
writer=csv.writer(f, delimiter=",",lineterminator="\n")
for toN in range(exc_neuron_num):
for ... | {"hexsha": "f48f3491f03e1d3f9d40006badd23e3747f1890f", "size": 606, "ext": "py", "lang": "Python", "max_stars_repo_path": "Fig2/hebbian_noSTDmod/create_1Dneuralfield_network.py", "max_stars_repo_name": "TatsuyaHaga/reversereplaymodel_codes", "max_stars_repo_head_hexsha": "503d545449efab603e18d224fc2f94158d967530", "max... |
'''
IKI Bangladesh (MIOASI): Data processing functions
Data processing functions relating to the IKI Bangladesh project.
Note: Python 3 compatible only
Author: HS
Created: 7/3/19
'''
import datetime
import math
import iris
import pandas as pd
import re
import time
import numpy as np
import netCDF4 as nc
import iris.... | {"hexsha": "52bc0022b226d88b4aedb6161b4a87ca31b01a2a", "size": 15913, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/dataprocessing.py", "max_stars_repo_name": "MetOffice/IKI-Oasis-Bangladesh", "max_stars_repo_head_hexsha": "a280be8a151b395c0117e700a259b37948faa3f2", "max_stars_repo_licenses": ["CC-BY-4.... |
/*
** Copyright (C) 2013 Aldebaran Robotics
** See COPYING for the license
*/
#include <qi/jsoncodec.hpp>
#include <qi/anyvalue.hpp>
#include <iterator>
#include <boost/lexical_cast.hpp>
#ifdef WITH_BOOST_LOCALE
// Disable deprecation warnings about `std::auto_ptr`.
# define BOOST_LOCALE_HIDE_AUTO_PTR
# include <b... | {"hexsha": "a11b9a051c300b5ea0816bff32c93ec132d09fe7", "size": 8524, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/type/jsondecoder.cpp", "max_stars_repo_name": "Maelic/libqi", "max_stars_repo_head_hexsha": "0a92452be48376004e5e5ebfe2bd0683725d033e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
"""Data Cleaning
This script allows the user to process data cleaning for both CoachingMate data and Garmin data.
This script requires that `pandas`, `numpy` be installed within the Python
environment you are running this script in.
This file can also be imported as a module
"""
# Packages
import numpy as np
import ... | {"hexsha": "f9e81fffd52de5e976103b42c57e9fa28f24b858", "size": 43792, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/data_cleaning.py", "max_stars_repo_name": "jamesxwang/monitoring-athletes-performance", "max_stars_repo_head_hexsha": "7df504711202a31a408ae1072ca5e8fbaf1cb5f9", "max_stars_repo_licenses": [... |
x <- r"(hello
"world")
| {"hexsha": "653260230d2e276241bd7291e64c247d5fb15691", "size": 23, "ext": "r", "lang": "R", "max_stars_repo_path": "testData/parser/r/UnclosedRawString.r", "max_stars_repo_name": "DeagleGross/Rplugin", "max_stars_repo_head_hexsha": "8a2cfd87f732e658b3de07a202c058a9a9d63f11", "max_stars_repo_licenses": ["MIT", "BSD-2-Cl... |
'''
predict.py有几个注意点
1、无法进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。
2、如果想要保存，利用r_image.save("img.jpg")即可保存。
3、如果想要原图和分割图不混合，可以把blend参数设置成False。
4、如果想根据mask获取对应的区域，可以参考detect_image中，利用预测结果绘图的部分。
seg_img = np.zeros((np.shape(pr)[0],np.shape(pr)[1],3))
for c in range(self.num_classes):
seg_i... | {"hexsha": "f25bf1ba1912946d8b805e8d9f15074024749c5e", "size": 3474, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "waterkingest/Myself_Unet_work", "max_stars_repo_head_hexsha": "e7c3b85ee2d04daf522eb27ca8de9ad720de0884", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
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