text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
# In[2]:
def sn_random_numbers(shape,antithetic=True,moment_matching=True,fixed_seed=False):
if fixed_seed:
np.random.seed(1000)
if antithetic:
ran=np.random.standard_normal((shape[0],shape[1],shape[2]//2))
ran=np.c... | {"hexsha": "1d2a19a4c0310c806f8cc0293db1004214c22d2c", "size": 580, "ext": "py", "lang": "Python", "max_stars_repo_path": "sn_random_numbers.py", "max_stars_repo_name": "HaoLiNick/quantbasic", "max_stars_repo_head_hexsha": "59ff8bef07df2357cc91e7092c8cb660285541b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from scripts.python.routines.plot.save import save_figure
from scripts.python.routines.plot.bar import add_bar_trace
from scripts.python.routines.plot.layout import add_layout
from scripts.python.routines.manifest import get_manifest
from scripts.... | {"hexsha": "4728111b674af385f9681a20c3b37ecb18441f38", "size": 1978, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/python/meta/tasks/GPL13534_Blood/005_plot_models_results.py", "max_stars_repo_name": "AaronBlare/dnam", "max_stars_repo_head_hexsha": "4d97c879cb24447eee0852eaf48fc5b3ef8e159b", "max_stars... |
export pca_session
function pca_session(spikes::Array{SpikeTrain,1},binsize::Float64,kern::Float64)
myrate=rate_KD(spikes,binsize,kern)
pcamat=zeros(Float64,length(spikes[1].trials[1].time:binsize:spikes[1].trials[end].time)-1,length(spikes))
for i=1:length(spikes)
pcamat[:,i]=rate_session(myrate,i... | {"hexsha": "0df77cddf0f0e8a1218b98bac27ba3cf36120dd1", "size": 1005, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/dimensionreduce.jl", "max_stars_repo_name": "paulmthompson/Spikes.jl", "max_stars_repo_head_hexsha": "43de3dfc6ec1fc9a1fcf93231590e8d98a85db4e", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
import numpy as np
class MixupImageDataGenerator():
def __init__(self, generator, directory, batch_size, img_height, img_width, alpha=0.2, subset=None):
"""Constructor for mixup image data generator.
Arguments:
generator {object} -- An instance of Keras ImageDataGenerator.
... | {"hexsha": "1c69b991500fba436e2e9d760606ee6de53510ae", "size": 3780, "ext": "py", "lang": "Python", "max_stars_repo_path": "mixup_generator.py", "max_stars_repo_name": "Tony607/keras_mixup_generator", "max_stars_repo_head_hexsha": "a510b3f7f909055b64a308bb8e94dc9e0d66e29c", "max_stars_repo_licenses": ["MIT"], "max_star... |
using Documenter, OneHotArrays
DocMeta.setdocmeta!(OneHotArrays, :DocTestSetup, :(using OneHotArrays); recursive = true)
makedocs(sitename = "OneHotArrays", doctest = false,
pages = ["Overview" => "index.md",
"Reference" => "reference.md"])
deploydocs(repo = "github.com/FluxML/OneHotArrays.... | {"hexsha": "79336a244f55e2a216a57477f27240bf020b09e2", "size": 390, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "FluxML/OneHotArrays.jl", "max_stars_repo_head_hexsha": "d44a239928bdb14517cdb9c1963d1deeed31c714", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
#include "RateManagerAsync.h"
#include <iostream>
#include <boost/bind/bind.hpp>
RateManagerAsync::RateManagerAsync(boost::asio::io_service & ioService, const uint64_t rateBitsPerSec, const uint64_t maxPacketsBeingSent) :
m_ioServiceRef(ioService),
m_rateTimer(ioService),
m_rateBitsPerSec(rateBitsPerSec),
... | {"hexsha": "80dee870495130047f0047fef56b8d3ce3608206", "size": 10089, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "common/util/src/deprecated/RateManagerAsync.cpp", "max_stars_repo_name": "ewb4/HDTN", "max_stars_repo_head_hexsha": "a0e577351bd28c3aeb7e656e03a2d93cf84712a0", "max_stars_repo_licenses": ["NASA-1.3... |
from tensorflow.keras import layers, Model, backend
import numpy as np
import tensorflow as tf
from signal_separation._signal_creator import destandardize_height, height_ratio, places_change, de_standardize
def multiply_cnn(n, kernel_size, added_filters, filter_beg, dense1, dense2, x, batch_n):
for i in np... | {"hexsha": "8ee3909706522f6617a40ed3f54811c35afd855b", "size": 8085, "ext": "py", "lang": "Python", "max_stars_repo_path": "signal_separation/_neural_network.py", "max_stars_repo_name": "MieszkoP/signal_separation", "max_stars_repo_head_hexsha": "c18a29fb92891e671907524609162f9a7985eaff", "max_stars_repo_licenses": ["M... |
import random
import numpy as np
import torch
# %%
def choice(a, size=None, replace=True):
'''
Randomly choose elements from given iterable.
Parameters
----------
a: int or list
choice of
'''
m = len(a)
# Single sample
if size==None:
random_i = np.random.randint(m... | {"hexsha": "ca61ec76bf3c33a9a1317a0b10879155917e6e18", "size": 1242, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/random.py", "max_stars_repo_name": "jsyoo61/tools", "max_stars_repo_head_hexsha": "bba8bfe69f87c8111d8b82c90a65a71b6996f9b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_s... |
# Copyright (c) 2014-2016, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions... | {"hexsha": "d6e4ac6356ccd2286079cb8aa4176a766b166f6b", "size": 4842, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymic/_misc.py", "max_stars_repo_name": "01org/pyMIC", "max_stars_repo_head_hexsha": "f775239a208dc1daaf89451af06e0138b80099e9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 38,... |
import torch
import torch.nn as nn
# from pts3d import *
import torchvision.models as models
import functools
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn import init
import numpy as np
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0),... | {"hexsha": "b2ce34f11ca517dd963c872256f504feb44c3c70", "size": 3344, "ext": "py", "lang": "Python", "max_stars_repo_path": "id_removing_net.py", "max_stars_repo_name": "keetsky/VGGVox_EBT", "max_stars_repo_head_hexsha": "fc2647b9bafc641d38b3f996205cdb8860f66625", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import random
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def randomized_response(true_value):
res1 = random.randint(0,1)
if (res1 == 1):
return true_value
else:
res2 = random.randint(0,1)
if (res2 == 1):
return 0
else:
return 1
n_users = 30
users = [i for i in range(100,... | {"hexsha": "3a6c1df1b494b89e92739ad9493fa4a6cd83ebc2", "size": 752, "ext": "py", "lang": "Python", "max_stars_repo_path": "LDP/random_response.py", "max_stars_repo_name": "nikosgalanis/bsc-thesis", "max_stars_repo_head_hexsha": "b5521e995f266ff1aeb9fecc220650483630dc04", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
# -*- coding: utf-8 -*-
import os
import sys
#lib_path = os.path.abspath('/home/dani/github/ConcursoPolicia')
#if lib_path not in sys.path:
# sys.path.append(lib_path)
import luigi
from RecolectorTwitter import *
from Config.Conf import Conf
from DBbridge.ConsultasCassandra import ConsultasCassandra
from DBbridge.Con... | {"hexsha": "aae57a5a54c993a11e18f9cc26e853b988959815", "size": 12209, "ext": "py", "lang": "Python", "max_stars_repo_path": "LuigiTasks/GenerateSim.py", "max_stars_repo_name": "garnachod/ConcursoPolicia", "max_stars_repo_head_hexsha": "f123595afc697ddfa862114a228d7351e2f8fd73", "max_stars_repo_licenses": ["Apache-2.0"]... |
# SM.Simp_AP.py
#
# Implements the Simplified Set-membership Affine-Projection algorithm for COMPLEX valued data.
# (Algorithm 6.7 - book: Adaptive Filtering: Algorithms and Practical
# Implementation, Diniz)
#
# Authors:
# . Bruno Ramos Lima N... | {"hexsha": "48db8edfcdd565099ccb41be270f6b36aee9b024", "size": 5302, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydaptivefiltering/SM/Simp_AP.py", "max_stars_repo_name": "BruninLima/PydaptiveFiltering", "max_stars_repo_head_hexsha": "14f4758a25b7cb3f1fd643caa5caffd5e7f06c6a", "max_stars_repo_licenses": ["RS... |
# Copyright(c) 2020 Jake Fowler
#
# 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, merge, publish, distri... | {"hexsha": "f51090510e79a9928eaae8b1927e4eca596a9c0d", "size": 17378, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Cmdty.Storage.Python/tests/test_multi_factor.py", "max_stars_repo_name": "eliot-tabet/storage", "max_stars_repo_head_hexsha": "b40c04ce49130e976b095d0087d2520bb9a80db2", "max_stars_repo_licen... |
# This is the R2-IBEA weight vector generation algorithm as described in:
# Dung H. Phan and Junichi Suzuki, "R2-IBEA: R2 Indicator Based Evolutionary
# Algorithm for Multiobjective Optimization", 2013.
function generate_random_weight_vector(m)
x = sort(rand(m-1))
w = zeros(m)
w[1] = x[1]
for i in 2:(m-1)
... | {"hexsha": "919cc3b483227fa753cd04598e2fa4a86287fedd", "size": 2795, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "spikes/r2_ibea_weight_vector_generation.jl", "max_stars_repo_name": "devmotion/BlackBoxOptim.jl", "max_stars_repo_head_hexsha": "252b373da6571b209f82660839923add60eba34d", "max_stars_repo_licenses"... |
%labels segments comparing their eventIdx property to event annotations.
%The eventIdx prperty is set if an eventSegmentation has been used
%to generate the segment
classdef EventSegmentsLabeler < Algorithm
properties (Access = public)
manualAnnotations;
end
methods (Access = public)
... | {"author": "avenix", "repo": "WDK", "sha": "c525222b02bd390b4758d30f1cd8b19af043108e", "save_path": "github-repos/MATLAB/avenix-WDK", "path": "github-repos/MATLAB/avenix-WDK/WDK-c525222b02bd390b4758d30f1cd8b19af043108e/ARC/algorithm/5-labeling/EventSegmentsLabeler.m"} |
[STATEMENT]
lemma WT_gpv_pauses [WT_intro]:
"\<I> \<turnstile>g pauses xs \<surd>" if "set xs \<subseteq> outs_\<I> \<I>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<I> \<turnstile>g pauses xs \<surd>
[PROOF STEP]
using that
[PROOF STATE]
proof (prove)
using this:
set xs \<subseteq> outs_\<I> \<I>
goal (1 su... | {"llama_tokens": 185, "file": "Constructive_Cryptography_CM_Fold_Spmf", "length": 2} |
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import models, transforms
import os
from sklearn import metrics
from data_utils import ImageFolderWithPaths
import pandas as pd
import torchnet.mete... | {"hexsha": "5c9c7e5fa4ab5b0de5bac821b5ea4a70ccbe1c21", "size": 9775, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/test.py", "max_stars_repo_name": "gonzalezsieira/UGR-FuCiTNet", "max_stars_repo_head_hexsha": "0bcf9ba493e8f1173661f88b397113dd386a1e9b", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import math
def standard_propensity(rxn, CRS, concentrations):
''' Standard Propensity function calculates propensity as the concentrations of the reactants raised to their coefficients
Arguements:
- rxn: Reaction object
- CRS: CRS object for system
- concentrations: list of molec... | {"hexsha": "97f1ae515177775c8a1be7b14938d12ae209fc79", "size": 5625, "ext": "py", "lang": "Python", "max_stars_repo_path": "chemevolve/PropensityFunctions.py", "max_stars_repo_name": "ELIFE-ASU/chemevolve", "max_stars_repo_head_hexsha": "3ab58024f2d32066c4ae102841de5f581dd4720f", "max_stars_repo_licenses": ["MIT"], "ma... |
using jInv.Mesh
using jInv.Utils
using jInv.ForwardShare
using ShapeReconstructionPaLS.Utils
using ParamLevelSet
using ShapeReconstructionPaLS.ShapeFromSilhouette;
using Statistics
using Distributed
using LinearAlgebra
using SparseArrays
using Test
println("Test SoftMax")
n = 20;
m = 10;
u = rand(20).-0.1;
At = sprand... | {"hexsha": "028dd8b8e75a513a07ca4c42837ce997837d840d", "size": 842, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/ShapeFromSilhouette/testSoftMax.jl", "max_stars_repo_name": "BGUCompSci/ShapeReconstructionPaLS", "max_stars_repo_head_hexsha": "725cfa2a2ab357b4f2ed564eb2227158efc07f7f", "max_stars_repo_licen... |
vidname = 'vid.mp4'
style = 's1.jpg'
import numpy as np
from PIL import Image
import random
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.keras.optimizers import *
import tensorflow.keras.backend as K
from tensorflow.keras.applications.vgg19 import VGG19
model_num = 90
... | {"hexsha": "0b71abd47fd27055120422f30e0754a6cfdc716e", "size": 2468, "ext": "py", "lang": "Python", "max_stars_repo_path": "video.py", "max_stars_repo_name": "manicman1999/Style-Transfer-TF2.0", "max_stars_repo_head_hexsha": "f9c4621320255a3d3ab150756f63ea0bae23f498", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
SUBROUTINE CheckTime(stime,etime,ctime)
IMPLICIT NONE
! -------------------------------------------------------------------------
! NAME: CheckTime
! STATUS: Current
! OWNER: General
! TEXT: Print CPU time.
! When called for the first time, stime should be set to 0.0 before
! ... | {"hexsha": "820520353b6ec4c078759ff7d3c2b8b2e3e0f894", "size": 762, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "CheckTime.f90", "max_stars_repo_name": "anducnguyen/ferritas_e", "max_stars_repo_head_hexsha": "e3273c5513d6f22b060b4ea9034022b07785746f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
subroutine runCY_00ll(k,l,Xtwiddle,Gtwiddle,Shat4,N0)
implicit none
C--- Expression for Eq. 5.58a
C--- Calculates C00ll
C--- Small terms of order Xtwiddle(0,k)*Ciii,Xtwiddle(0,0)*Ciiii
C--- Denominator Gtwiddle(k,l)
include 'pvCnames.f'
include 'pvCv.f'
include 'Carraydef.f'
i... | {"hexsha": "d3f4956e636a0a9742da5a27124ccec60c6cedae", "size": 793, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/TensorReduction/recur/smallY/runCY_00ll.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses"... |
module FxForHasql.Prelude
(
module Exports,
)
where
-- base
-------------------------
import Control.Applicative as Exports
import Control.Arrow as Exports hiding (first, second)
import Control.Category as Exports
import Control.Concurrent as Exports
import Control.Exception as Exports
import Control.Monad as Expor... | {"hexsha": "48e3f4b6fc6ce29324b4a83ef6088d050b2ec8e0", "size": 3317, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "library/FxForHasql/Prelude.hs", "max_stars_repo_name": "nikita-volkov/fx-for-hasql", "max_stars_repo_head_hexsha": "fc515853d0d32376874df29663cb7eaf3e2d44e4", "max_stars_repo_licenses": ["MIT"], ... |
\documentclass[11pt,twoside]{article}
\usepackage[headings]{fullpage}
\usepackage[utopia]{mathdesign}
\pagestyle{myheadings}
\markboth{Horn equation}{Horn equation}
\input{../../fncextra}
\begin{document}
\begin{center}
\bf Toot your own horn
\end{center}
The use of mathematics for the design and analysis ... | {"hexsha": "6ae51825580f02adfa160133a2235298341cf9af", "size": 5254, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "labs/chapter10/Horn/Horn.tex", "max_stars_repo_name": "snowdj/fnc-extras", "max_stars_repo_head_hexsha": "ef51fada748de1326a4ce645fbcb0c2499cb2b8a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# Import libraries
import argparse
from azureml.core import Run
import pandas as pd
import numpy as np
import joblib
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
# Set ... | {"hexsha": "fc1775ab8a72168fbd42e4f7efcfa3c805f32d47", "size": 1914, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/mslearn-aml-labs/diabetes_training_from_tab_dataset/diabetes_training.py", "max_stars_repo_name": "Code360In/data-science", "max_stars_repo_head_hexsha": "cb6093e898ccb860e76914057a52f751... |
# -*- coding: utf-8 -*-
import sys
import types
import os
import getopt
import re
from pathlib import Path
from pathlib import PurePath
from io import StringIO
import contextlib
import importlib
from lopper import Lopper
from lopper import LopperFmt
import lopper
from lopper_tree import *
from re import *
import numpy ... | {"hexsha": "748db52ed8aa84cc6bfe891e154db0bbabe574b8", "size": 129423, "ext": "py", "lang": "Python", "max_stars_repo_path": "assists/zuplus_config.py", "max_stars_repo_name": "nagasureshkumar/lopper", "max_stars_repo_head_hexsha": "8c5f34181a246cdd8ed8ed4ba6e32de017940af8", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import numpy as np
import pandas as pd
import tensorflow as tf
import math
data = pd.read_excel('1000_abstracttotal.xls', encoding='utf-8')
data = data.iloc[:,[0,2]]
raw_data = data.to_numpy()
#print(raw_data.shape)
x=raw_data[:,1].reshape(len(raw_data),1)
y=raw_data[:,0].reshape(len(raw_data),1)
#print(x.sha... | {"hexsha": "22255bc3c9f871db2314ebd1403e9d9c815a494a", "size": 851, "ext": "py", "lang": "Python", "max_stars_repo_path": "bert_classifier/read_made.py", "max_stars_repo_name": "Zeng-WH/MaterBERT", "max_stars_repo_head_hexsha": "92bdf721ee168a3c91c6cd94599b7df65f9e6cbd", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import pandas as pd
from collections import OrderedDict
from datetime import datetime
CRA = 0.001
MAX_ERROR = 0.0000000001
MAX_RUNS = 20
def DiscountedValue4par2forwards(
sum_df: float = 0,
last_df: float = 0,
par_rate: float = 0,
forward_rate: float = 0,
t_min_k: int = 0,
) ->... | {"hexsha": "b50499e939bff5eab4630235b8cbaa32a6a4bdfe", "size": 3084, "ext": "py", "lang": "Python", "max_stars_repo_path": "solvency2_data/alternative_extrapolation.py", "max_stars_repo_name": "DeNederlandscheBank/solvency2-rfr", "max_stars_repo_head_hexsha": "61f192b98283274594f80605b11823a0a505d0f6", "max_stars_repo_... |
''' Define the Transformer model '''
import torch
import torch.nn as nn
import numpy as np
#import transformer.Constants as Constants
#from transformer.Layers import EncoderLayer#, DecoderLayer
import Constants as Constants
from Layers import EncoderLayer#, DecoderLayer
##############################################... | {"hexsha": "43f5bb80c5dd914424553094d748fdc9e8c5a1b4", "size": 5289, "ext": "py", "lang": "Python", "max_stars_repo_path": "transformer/Models.py", "max_stars_repo_name": "hamed-sadeghi-layer6/transformer-kk-mimic", "max_stars_repo_head_hexsha": "583c6aa00c97724f36bc196cd619f7a8b7928fd8", "max_stars_repo_licenses": ["M... |
import numpy as np
import scipy as sp
import scipy.optimize
import scipy.integrate
import scipy.special
import matplotlib.pyplot as plt
import math
###############################################################
################# Use Only These Functions ####################
###########################################... | {"hexsha": "58364d1713f3f48ae9178d0c88913d7a78d75723", "size": 4119, "ext": "py", "lang": "Python", "max_stars_repo_path": "kernel_generalization/kernel_simulation.py", "max_stars_repo_name": "Pehlevan-Group/kernel_regression", "max_stars_repo_head_hexsha": "761c2e9f72da204ca1c4ef0841a97caf8f306d23", "max_stars_repo_li... |
[STATEMENT]
lemma subcls1_induct [consumes 1]:
"\<lbrakk>ws_prog G; \<And>x. \<forall>y. (x, y) \<in> subcls1 G \<longrightarrow> P y \<Longrightarrow> P x\<rbrakk> \<Longrightarrow> P a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>ws_prog G; \<And>x. \<forall>y. G\<turnstile>x\<prec>\<^sub>C1y \<longr... | {"llama_tokens": 577, "file": null, "length": 5} |
```python
def downloadDriveFile(file_id,file_name,file_extension):
'''
Allows charge of public files into colab's workspace
'''
!wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate '... | {"hexsha": "f86f5801d0ba2fa9975027b07a5c3b1f0e4a3f8a", "size": 36687, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "numeric_analysis_exercises/cap3_set_constrained_optimization.ipynb", "max_stars_repo_name": "lufgarciaar/num_analysis_exercises", "max_stars_repo_head_hexsha": "d145908494c5a7453830e... |
from task1 import get_Lagrange_descr
from sympy import symbols, diff
x1, x2, x3, t = symbols('x1 x2 x3 t')
def get_velocity_Lagrange(eq1, eq2, eq3):
U1, U2, U3 = get_Lagrange_descr(eq1, eq2, eq3)
V1 = diff(U1, t)
V2 = diff(U2, t)
V3 = diff(U3, t)
return [V1, V2, V3]
def get_acceleration_Lagrange(... | {"hexsha": "590614ee5ce3789bed616913ca070d76779f73ce", "size": 608, "ext": "py", "lang": "Python", "max_stars_repo_path": "pkmkt2_code/task2.py", "max_stars_repo_name": "toomastahves/predicting-diabetes", "max_stars_repo_head_hexsha": "ed7d3f9e64970fe0aacd13db9afb707f428ed2ac", "max_stars_repo_licenses": ["MIT"], "max_... |
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.models import inception_v3
import numpy as np
from scipy import linalg
from skimage.metrics import peak_signal_noise_ratio as compare_psnr
from skimage.metrics import structural_similarity as ssim
from pytorch_lightning.metrics impo... | {"hexsha": "57249fe1e2a48dc101a1d545aa7051b648fef774", "size": 12279, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/metrics.py", "max_stars_repo_name": "CompVis/interactive-image2video-synthesis", "max_stars_repo_head_hexsha": "05ea449d3a2704b6d79a5f08683035220d615576", "max_stars_repo_licenses": ["MIT"]... |
from sympy import *
n = abs(int(input()))
if isprime(n):
print('This number is prime')
else:
print('This number is not prime') | {"hexsha": "72c719271848e7146dbfaa871ed9dadf6aa9e6a3", "size": 135, "ext": "py", "lang": "Python", "max_stars_repo_path": "self-learning/based/00000028.py", "max_stars_repo_name": "vladspirin/python-learning", "max_stars_repo_head_hexsha": "6b005fb28f96c0d610348a0b5f8830f94c53075f", "max_stars_repo_licenses": ["Unlicen... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright CNRS 2012
# Roman Yurchak (LULI)
# This software is governed by the CeCILL-B license under French law and
# abiding by the rules of distribution of free software.
import sys
import os, os.path
import warnings
import numpy as np
from hedp.math.abel import abel
fro... | {"hexsha": "7709a753708cf94fc9bb784c246d98e3338746dd", "size": 1118, "ext": "py", "lang": "Python", "max_stars_repo_path": "hedp/pp/interferometry.py", "max_stars_repo_name": "luli/hedp", "max_stars_repo_head_hexsha": "ab78879106ef2d7b6e54ac6a69d24439ec8c9a8b", "max_stars_repo_licenses": ["CECILL-B"], "max_stars_count"... |
[STATEMENT]
lemma "|x \<cdot> y] q = |x] |y] q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. |x \<cdot> y] q = |x] |y] q
[PROOF STEP]
using fbox_mult
[PROOF STATE]
proof (prove)
using this:
|?x \<cdot> ?y] ?z = |?x] |?y] ?z
goal (1 subgoal):
1. |x \<cdot> y] q = |x] |y] q
[PROOF STEP]
. | {"llama_tokens": 164, "file": "Hybrid_Systems_VCs_ModalKleeneAlgebra_HS_VC_MKA", "length": 2} |
module ObjectTileNames
names = Dict{Int, String}(
-1 => "Air (-1)",
0 => "Grass A (0)",
1 => "Grass B (1)",
2 => "Grass C (2)",
3 => "Grass D (3)",
8 => "Fence Top A (8)",
9 => "Fence Top B (9)",
10 => "Fence Top C (10)",
11 => "Fence Top D (11)",
12 => "Fence Top E (12)",
1... | {"hexsha": "73830da5aaf67d5b0092a69f8838fba5942ec344", "size": 6204, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/object_tile_names.jl", "max_stars_repo_name": "kingW3/Ahorn", "max_stars_repo_head_hexsha": "a064bf0a953620415ea6eb8b0d610c8c606b2b05", "max_stars_repo_licenses": ["FSFAP"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 2 14:51:27 2016
@author: DanielleTump
Note: This script is not run in full, but is ran by parts of it,
depending on the ensemble needed and the files used.
"""
import csv #to read from/write to csv files
from math import ceil #to round floats to the highest integer
... | {"hexsha": "7a42e6dcd2cdd7fef3240d9843fb0fc3d536d14b", "size": 11230, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project2/Code/Ensemble/EnsembleTrainsetVerificationset.py", "max_stars_repo_name": "TheLaurens/Team-Brains", "max_stars_repo_head_hexsha": "78b2417c90116336ac02036d4c148eb221f73830", "max_stars_r... |
/*
+----------------------------------------------------------------------+
| HipHop for PHP |
+----------------------------------------------------------------------+
| Copyright (c) 2010-2014 Facebook, Inc. (http://www.facebook.com) |
+---------... | {"hexsha": "f5fac491d4c0d0c63f545fe6d1476ec2fd1307a9", "size": 13399, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "hphp/compiler/option.cpp", "max_stars_repo_name": "nareshv/hhvm", "max_stars_repo_head_hexsha": "3528dc973dec02a4cc18a10586ce485995818666", "max_stars_repo_licenses": ["PHP-3.01", "Zend-2.0"], "max... |
#ifdef WITH_PYTHON
// Don't compile... anything, otherwise.
#include <functional>
#include <solvers/smt/smt_conv.h>
#include <boost/python.hpp>
#include <boost/python/class.hpp>
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include <solve.h>
#include <smt_python.h>
class dummy_solver_class
{
};
cl... | {"hexsha": "87741d816053d61e886865fefe43907f11b626b8", "size": 33807, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/solvers/smt_python.cpp", "max_stars_repo_name": "alecs184/esbmc", "max_stars_repo_head_hexsha": "ec70901e554b8fdcfaa82b85a7050fa042168ca7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
"""
Data Prep Script by Mary Kohl
Adapted from 2020-06-07_prep_sources_DLevitt.py
"""
import pandas as pd
import numpy as np
import os
import re
in_dir = '../food-data/PFPC_data_files'
out_dir = '../food-data/Cleaned_data_files'
in_path = os.path.join(in_dir,'FMNPMarkets.xlsx')
out_path = os.path.join(out_dir,'FMNPM... | {"hexsha": "f8259dffd7e5af3f4b10d314158ec510344ebe25", "size": 2690, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_prep_scripts/2020-10-20_prep_sources_MKohl.py", "max_stars_repo_name": "marykohl3/food-access-map-data", "max_stars_repo_head_hexsha": "29547f63d59691069994aaf9949c0e2b13e90be6", "max_stars_r... |
abstract type SequenceSpec end
# All all elements
mutable struct SequenceAll <: SequenceSpec
end
# None no elements
mutable struct SequenceNone <: SequenceSpec
end
# n elements 1 through n
struct SequenceN{T<:Integer} <: SequenceSpec
n::T
end
# UpTo[n] elements 1 up to at most n
struct SequenceUpToN{T<:In... | {"hexsha": "db0f8a7101936061088a4f0c3648467fc46e646b", "size": 2286, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/sequence_specification.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Symata.jl-a906b1d5-d016-55c4-aab3-8a20cba0db2a", "max_stars_repo_head_hexsha": "a717c629b2bcce4f78f3956752a2e5... |
# --- Binary managment
# ----------------------------------------------------
# --- Modules
# ----------------------------------------------------
using Random
# ----------------------------------------------------
# --- Source code
# ----------------------------------------------------
"""
---
Create a ... | {"hexsha": "08bfcf4d0d3fc22d18f6ee73d0579c63ad33dda7", "size": 3349, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/genBitSequence.jl", "max_stars_repo_name": "JuliaTelecom/DigitalComm.jl", "max_stars_repo_head_hexsha": "13c854b9c4a8864787075e06e0e3ef5a1d30beae", "max_stars_repo_licenses": ["MIT"], "max_star... |
import miepy
import numpy as np
def test_plane_wave_point_matching():
"""point matching a plane wave agrees with analytic results"""
k = 1
wav = 2*np.pi/k
lmax = 3
source = miepy.sources.plane_wave([1,0])
p_src = source.structure([[0,0,0]], k, lmax)[0]
p_src_numeric = miepy.vsh.decomposi... | {"hexsha": "78589141af6ec0ceb3e9313d55f136eb45bd017f", "size": 1263, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_point_matching.py", "max_stars_repo_name": "johnaparker/MiePy", "max_stars_repo_head_hexsha": "5c5bb5a07c8ab79e9e2a9fc79fb9779e690147be", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import io
import os
import zipfile
import numpy as np
from PIL import Image
from chainer.dataset import download
def get_facade():
root = download.get_dataset_directory('study_chainer/facade')
npz_path = os.path.join(root, 'base.npz')
url = 'http://cmp.felk.cvut.cz/~tylecr1/facade/CMP_facade_DB_base.zip'
... | {"hexsha": "ceb5d0bafb4c57f1cb4c6234e4bdd51d46f57db3", "size": 1190, "ext": "py", "lang": "Python", "max_stars_repo_path": "study_chainer/datasets/facade.py", "max_stars_repo_name": "briongloid/study_chainer", "max_stars_repo_head_hexsha": "95f2c7848f050302cac9f8875d24e8c200946e32", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import pandas as pd
import os
from kmodes import kmodes
from sklearn.cluster import KMeans, SpectralClustering
from sklearn.cluster import MiniBatchKMeans
import csv
from datetime import datetime
import time
import sys
from sklearn import metrics
from sklearn.metrics import pairwise_distances
cl... | {"hexsha": "ddfde66a8265479272ca955cf36fe7e7aa415ee5", "size": 2015, "ext": "py", "lang": "Python", "max_stars_repo_path": "servidor/machine_learning/clustering.py", "max_stars_repo_name": "FelipeLimaM/ItsMyLife-Framework", "max_stars_repo_head_hexsha": "c1d1ce89db1882a2594b126ac6407fca6d9255aa", "max_stars_repo_licens... |
[STATEMENT]
lemma rel_resumption_OO [relator_distr]:
"rel_resumption A B OO rel_resumption C D = rel_resumption (A OO C) (B OO D)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Resumption.resumption.rel_resumption A B OO Resumption.resumption.rel_resumption C D = Resumption.resumption.rel_resumption (A OO C) (B O... | {"llama_tokens": 149, "file": "CryptHOL_Resumption", "length": 1} |
import numpy as np
import pandas as pd
from scipy import optimize, special
from sklearn import metrics
def optimize_threshold_f1(outputs, labels):
std = np.std(outputs)
bounds = np.array([np.min(outputs), np.max(outputs)]) / std
def fn(thresh):
return -metrics.f1_score(labels, outputs >= std * thre... | {"hexsha": "0fb4ab37949ea0efef95e09d24acb0f82b21cca3", "size": 6969, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepSM/post_processing.py", "max_stars_repo_name": "Vivoe/DeepSM", "max_stars_repo_head_hexsha": "bc35f2bfc3758199466079ec54de1d5297374921", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 08:55:53 2020
@author: Manuel Pinar-Molina
"""
import numpy as np
'''
Normalize the original data with values between 0-255
'''
def normalize(original):
readdata_norm = np.array(original) + abs(np.min(original))
readdata_norm = readdata... | {"hexsha": "7934bb522184996175d06487b8d1ddc855893c0d", "size": 2006, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/seg_extend.py", "max_stars_repo_name": "manuelpinar/3DUnetCNN", "max_stars_repo_head_hexsha": "d40cf10ab2ee78c790caf4ebee6516f306797140", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
program test_get_grid_shape
use bmif_1_2, only: BMI_FAILURE
use bmisnowf
use fixtures, only: config_file, status
implicit none
integer, parameter :: grid_id = 0
integer, parameter :: rank = 2
integer, dimension(rank), parameter :: expected_shape = [1, 1]
type (bmi_snow) :: m
integer, dimension(2) ... | {"hexsha": "751d91cd1cbb29c2485d3e7026d0df42a042aef3", "size": 606, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "snow/tests/test_get_grid_shape.f90", "max_stars_repo_name": "wk1984/Snow_BMI_Fortran", "max_stars_repo_head_hexsha": "68040d7cdfabe95e3376d55383ecfbdcbc7f87cd", "max_stars_repo_licenses": ["Apach... |
import matplotlib.pyplot as plt
import numpy as np
from hypot import hypot
bs = np.linspace(0., 5., 10)
rs = [hypot(dict(a = 2., b = b)) for b in bs]
plt.plot([r["c"] for r in rs])
plt.show()
| {"hexsha": "77631ae2449f8ddef304f2a842a1f4b035e9da5e", "size": 194, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot.py", "max_stars_repo_name": "rekka/python-cpp-json", "max_stars_repo_head_hexsha": "6c39c2d7d7952f79c0533ea287d23a7c3ce1e6c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
\section{Method}
\label{sec:method}
In this section we describe how we calculate full 3D velocities for stars in
the Kepler field.
Around 1 in 3 Kepler targets have an RV from either Gaia, LAMOST, or APOGEE.
For these \nrv\ stars we calculated 3D velocities using the {\tt coordinates}
library of {\tt astropy} \citep{a... | {"hexsha": "7c5a5dbb87e6911994bcb19228f9f2a25482fac0", "size": 19932, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/method.tex", "max_stars_repo_name": "RuthAngus/kepler_kinematics", "max_stars_repo_head_hexsha": "cd8d3d0f9bc74ce2a39266ed2bac6a8f10499f64", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#! /usr/bin/env python
# Example scipt to show integration of a 1D spectrum
import nmrglue as ng
import numpy as np
import matplotlib.pyplot as plt
# read in the data from a NMRPipe file
dic, data = ng.pipe.read("1d_data.ft")
length = data.shape[0]
# read in the integration limits
peak_list = np.recfromtxt("limits.i... | {"hexsha": "53428f01ceb4c61aaf703b325c2d3e92689e33dd", "size": 1395, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/integration/integrate_1d/integrate_1d.py", "max_stars_repo_name": "genematx/nmrglue", "max_stars_repo_head_hexsha": "8a24cf6cbd18451e552fc0673b84c42d1dcb69a2", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma comp_wb_lens: "\<lbrakk> wb_lens x; wb_lens y \<rbrakk> \<Longrightarrow> wb_lens (x ;\<^sub>L y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>wb_lens x; wb_lens y\<rbrakk> \<Longrightarrow> wb_lens (x ;\<^sub>L y)
[PROOF STEP]
by (unfold_locales, auto simp add: lens_comp_def wb_lens_de... | {"llama_tokens": 161, "file": "Optics_Lens_Algebra", "length": 1} |
[STATEMENT]
lemma ideal_UNIV: "ideal UNIV"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ideal UNIV
[PROOF STEP]
unfolding ideal_def left_ideal_def right_ideal_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (subgroup UNIV \<and> (\<forall>x\<in>UNIV. \<forall>r. r * x \<in> UNIV)) \<and> subgroup UNIV \<and>... | {"llama_tokens": 284, "file": "Echelon_Form_Rings2", "length": 3} |
# generate data for tictactoe 4 board sizes ... 3 odd sized and one even
import utils as u
import numpy as np
import h5py
batch_size = 128
num_classes = 2
epochs = 40
num_random_matches = 1000000
print("generate data....")
print("3x3")
board_size = 3
xinput = u.generateGameDataUsingRnd(board_size, num_random_match... | {"hexsha": "fc20e6f502eed491d5f19751b60ca51d1821abe6", "size": 2450, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/generate_tic_tac_toe_data.py", "max_stars_repo_name": "TRex22/vector-AI-ML-term_project", "max_stars_repo_head_hexsha": "70d1cab20966c9c71e38a2f471d60628396c8e5a", "max_stars_repo_licenses": [... |
from PIL import Image
import numpy as np
import ast
import cv2
f = open("./source/prototypes/testArray/test.txt", "r")
iar = ast.literal_eval(f.read())
rows = len(iar)
columns = len(iar[0])
newImg = []
# newImg = iar
x = 0
y = 0
while y < rows:
newImg.append([])
while x < columns:
if iar[y][x] == 0:... | {"hexsha": "98ed962561d0ad4342a8ba6156287021595bf1cf", "size": 1390, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/prototypes/testArray/test.py", "max_stars_repo_name": "JonasHimmetsbergerStudent/ScribbleFight", "max_stars_repo_head_hexsha": "b896a152b26fde5a57cd72cea074c952c4ea0de9", "max_stars_repo_li... |
\section{Hierarchization on Full Grids (Unidirectional Principle)}
\label{sec:42fullGrids}
If $\sgset$ is a full grid $\fgset{\*l}$
(see \cref{sec:21nodalSpaces}),
the well-known \up
can be used to apply $\linop$ to input data $\vlinin$.
As shown in \cref{fig:unidirectionalPrinciple} for a sparse grid,
the idea of the... | {"hexsha": "de97606aceccb4710571f00a9be9674e18e0afc5", "size": 8452, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/document/42fullGrids.tex", "max_stars_repo_name": "valentjn/thesis-arxiv", "max_stars_repo_head_hexsha": "ae30179e67cd6a7813385e140b609546fd65b897", "max_stars_repo_licenses": ["CC0-1.0"], "max_... |
import scipy as sp
from ConfigParser import SafeConfigParser
config = SafeConfigParser()
config.read('xdmft.in')
###########################################################
# read the inputs from xdmft.in ###########################
# mpi part ################################################
np = 6
if config.has_op... | {"hexsha": "1adc879f9a5e7eed616fb21b21b4263917bca5b9", "size": 3053, "ext": "py", "lang": "Python", "max_stars_repo_path": "params.py", "max_stars_repo_name": "pokornyv/linkTRIQS_2bH", "max_stars_repo_head_hexsha": "8bdc689ef4a3a2ab5ee8de1f6cc66af8406980e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import pandas as pd
import numpy as np
from pathlib import Path
from datetime import datetime as dt
def mergePeople(IDColumn, gameLogs, people):
merged = pd.merge(gameLogs[['row','Date',IDColumn]], people, how="left", left_on=[IDColumn], right_on=['playerID'])
merged['age'] = (pd.to_datetime(merged['Date']) - ... | {"hexsha": "eb360523150aa6fa3dcfa6e4b470a1b52a1df7aa", "size": 1206, "ext": "py", "lang": "Python", "max_stars_repo_path": "Verworfen/~mlb_merge_people.py", "max_stars_repo_name": "timucini/MLB-DeepLearning-Project", "max_stars_repo_head_hexsha": "2737e9cd32edefec8ec935f304d04206264ce349", "max_stars_repo_licenses": ["... |
(**
CoLoR, a Coq library on rewriting and termination.
See the COPYRIGHTS and LICENSE files.
- Frederic Blanqui, 2005-06-17
general results on booleans
*)
Set Implicit Arguments.
From Coq Require Import Arith Lia.
From Coq Require Export Bool.
From Coq Require Setoid.
From CoLoR Require Import LogicUtil.
Argumen... | {"author": "sorinica", "repo": "spike-prover", "sha": "f2d6dd0bcebb647e09dd23048753075551da27eb", "save_path": "github-repos/coq/sorinica-spike-prover", "path": "github-repos/coq/sorinica-spike-prover/spike-prover-f2d6dd0bcebb647e09dd23048753075551da27eb/CoLoR/Coq8.16/BoolUtil.v"} |
import cv2
import os
import numpy as np
color_map = {
"Animal" : (64, 128, 64 ),
"Archway" : (192, 0, 128 ),
"Bicyclist" : (0, 128, 192 ),
"Bridge" : (0, 128, 64 ),
"Building" : (128, 0, 0 ),
"Car" : (64, 0,... | {"hexsha": "160485cd8aca3bc5dc912d7aca45eb0371f6b307", "size": 2127, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/test/convert_color_to_label.py", "max_stars_repo_name": "tiger0421/DDRNet.pytorch", "max_stars_repo_head_hexsha": "138cdc61c4cb00104f5051a129c31d603efb02ed", "max_stars_repo_licenses": ["M... |
#Jinliang yang
#Purpose: Use python code to merge snps from 3 datasets
#updated: 7.2.2014
#Note: Running on server 129.186.85.7
run_snp3merge <- function(chr="chr1"){
hmp1 <- paste("/mnt/02/yangjl/DBcenter/VariationDB/HapMap1/hapmap1V2_070214_", chr, ".dsf", sep="");
hmp2 <- paste("/mnt/02/yangjl/DBcenter/Variatio... | {"hexsha": "bd152aa7060f1a07edd2d0f4563789faadcf5e3e", "size": 1968, "ext": "r", "lang": "R", "max_stars_repo_path": "profiling/2.SNP/old/2.A.4_snp_merge.r", "max_stars_repo_name": "yangjl/Heterosis-GWAS", "max_stars_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_stars_repo_licenses": ["RSA-MD"], "... |
# On Eigendecomposition of Asset Returns
### Francisco A. Ibanez
## Part 1. $\Sigma$ v. $R$
To test some of the outstanding points on the stability of the eigendecomposition of asset returns, we will analyze simulated returns to avoid drawing conclusions from a specific dataset, which might not serve the general case.... | {"hexsha": "60262bec7b9e20bf4b173af094f4f12f8559bdd1", "size": 393968, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/robust_decomposition.ipynb", "max_stars_repo_name": "fcoibanez/eigenportfolio", "max_stars_repo_head_hexsha": "6e0f6c0239448a191aecf9137d545abf12cb344e", "max_stars_repo_l... |
import ngraph as ng
import numpy as np
import pytest
from ngraph.utils.types import get_element_type
from tests import xfail_issue_58033
from tests.runtime import get_runtime
def einsum_op_exec(input_shapes: list, equation: str, data_type: np.dtype,
with_value=False, seed=202104):
"""Test Eins... | {"hexsha": "fb7581d9160fa9f2b6115ff1cc2292db49e17925", "size": 3583, "ext": "py", "lang": "Python", "max_stars_repo_path": "runtime/bindings/python/tests/test_ngraph/test_einsum.py", "max_stars_repo_name": "monroid/openvino", "max_stars_repo_head_hexsha": "8272b3857ef5be0aaa8abbf7bd0d5d5615dc40b6", "max_stars_repo_lice... |
DOUBLE PRECISION FUNCTION slZD (HA, DEC, PHI)
*+
* - - -
* Z D
* - - -
*
* HA, Dec to Zenith Distance (double precision)
*
* Given:
* HA d Hour Angle in radians
* DEC d declination in radians
* PHI d observatory latitude in radians
*
* The result is in the range 0... | {"hexsha": "c7376266ba3a5a1a7ea8c35b2b9105a6b0f4cb01", "size": 2675, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "iraf.v2161/math/slalib/zd.f", "max_stars_repo_name": "ysBach/irafdocgen", "max_stars_repo_head_hexsha": "b11fcd75cc44b01ae69c9c399e650ec100167a54", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import caffe
import numpy as np
import ast
import base64
import csv
import random
import sys
import json
import atexit
from collections import defaultdict
csv.field_size_limit(sys.maxsize)
np.random.seed()
random.seed()
# Memory efficient version of rcnn_layers.py. If you have lots of RAM,
# training with rcnn_layer... | {"hexsha": "6b4abc4d48c8c5e41f15958d61ef525a314cc4b2", "size": 8132, "ext": "py", "lang": "Python", "max_stars_repo_path": "layers/efficient_rcnn_layers.py", "max_stars_repo_name": "quangvy2703/Up-Down-Captioner", "max_stars_repo_head_hexsha": "c7c4bc4a36d62f67fe21efdac3c1afcf63432977", "max_stars_repo_licenses": ["MIT... |
##########################################################################
# MediPy - Copyright (C) Universite de Strasbourg
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for de... | {"hexsha": "4edb3329828c4a371943eea6b7854752cb516805", "size": 7443, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/medipy/gui/annotations/image_annotation.py", "max_stars_repo_name": "bsavelev/medipy", "max_stars_repo_head_hexsha": "f0da3750a6979750d5f4c96aedc89ad5ae74545f", "max_stars_repo_licenses": ["CE... |
from __future__ import division
from __future__ import print_function
import numpy as np
from scipy.stats import rv_discrete, entropy
from copy import deepcopy
class Infinite2DgridAction(object):
def __init__(self, action):
self.action = action
self._hash = 10*(action[0]+2) + action[1]+2
def... | {"hexsha": "a6ce3ed5c7897e8f7a5c85893ac9a633d789d976", "size": 4701, "ext": "py", "lang": "Python", "max_stars_repo_path": "bamcp/states/infinite_2Dgrid_state.py", "max_stars_repo_name": "mspeekenbrink/mcts", "max_stars_repo_head_hexsha": "a3aac44a9697e9f67c6656dfc1dd3b91666a1633", "max_stars_repo_licenses": ["BSD-2-Cl... |
[STATEMENT]
lemma tensor_compose_distribution1:
assumes wf1:"mat (row_length A1) (length A1) A1"
and wf2:"mat (row_length A2) (length A2) A2"
and wf3:"mat (row_length B1) (length B1) B1"
and wf4:"mat (row_length B2) (length B2) B2"
and matchAA:"length A1 = row_length A2"
and matchBB:"length B1 = ro... | {"llama_tokens": 3907, "file": "Matrix_Tensor_Matrix_Tensor", "length": 29} |
[STATEMENT]
lemma disj_convr [simp]: "(p \<or> q)\<^sup>- = (q\<^sup>- \<or> p\<^sup>-)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (p \<or> q)\<^sup>- = (q\<^sup>- \<or> p\<^sup>-)
[PROOF STEP]
by (pred_auto) | {"llama_tokens": 108, "file": "UTP_utp_utp_rel_laws", "length": 1} |
import numpy as np
import pytest
from ddtruss import Truss, DataDrivenSolver
points = np.array([[0, 0], [1, 0], [0.5, 0.5], [2, 1]])
lines = np.array([[0, 2], [1, 2], [1, 3], [2, 3]], dtype=int)
truss = Truss(points, lines)
E = 1.962e11
A = [2e-4, 2e-4, 1e-4, 1e-4]
U_dict = {0: [0, 0], 1: [0, 0]}
F_dict = {3: [0, -... | {"hexsha": "e494747ad6589e1234241f26ac62dacfe6cecd8c", "size": 998, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_truss.py", "max_stars_repo_name": "deeepeshthakur/ddtruss", "max_stars_repo_head_hexsha": "86aa945d577c6efe752099eee579386762942289", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#ifndef STAN_MATH_REV_MAT_FUN_COV_EXP_QUAD_HPP
#define STAN_MATH_REV_MAT_FUN_COV_EXP_QUAD_HPP
#include <stan/math/rev/core.hpp>
#include <stan/math/rev/scal/fun/value_of.hpp>
#include <stan/math/prim/mat/fun/Eigen.hpp>
#include <stan/math/prim/scal/err/check_not_nan.hpp>
#include <stan/math/prim/scal/err/check_positiv... | {"hexsha": "4b0e16e53a8de7c8b5ae219c95450900d781846d", "size": 9033, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "stan/math/rev/mat/fun/cov_exp_quad.hpp", "max_stars_repo_name": "sakrejda/math", "max_stars_repo_head_hexsha": "3cc99955807cf1f4ea51efd79aa3958b74d24af2", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
[STATEMENT]
lemma lens_plus_eq_left: "\<lbrakk> X \<bowtie> Z; X \<approx>\<^sub>L Y \<rbrakk> \<Longrightarrow> X +\<^sub>L Z \<approx>\<^sub>L Y +\<^sub>L Z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>X \<bowtie> Z; X \<approx>\<^sub>L Y\<rbrakk> \<Longrightarrow> X +\<^sub>L Z \<approx>\<^sub>L Y +\<... | {"llama_tokens": 175, "file": "Optics_Lens_Order", "length": 1} |
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
def plot_inset_image(ax, x_pos, y_pos, filename, img_height = 1, img_width = -1, format = "png", ignore_aspect = False, return_fig_dimensions = False):
"""
Plot specified image on the given axis.
Only img_height or img_width is ne... | {"hexsha": "6999d69d4e08d76362f2b2a393739d58409dca60", "size": 2006, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_visualization/plot_inset_image.py", "max_stars_repo_name": "CashabackLab/DataVisualization", "max_stars_repo_head_hexsha": "91b2a2d6020ae2fb5b8277f5c7bca69d620be1cb", "max_stars_repo_licenses... |
module variableKind
!! Defines variable kinds
use, intrinsic :: iso_fortran_env, only: i8=>int8, i16=>int16, i32=>int32, i64=>int64
use, intrinsic :: iso_fortran_env, only: r32=>real32, r64=>real64
integer(i32), parameter :: cLen = 1024 !! Default character length for temporaries
end module
| {"hexsha": "46d095eb9b90e530757518a4d6b6399c36504128", "size": 312, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/core/m_variableKind.f90", "max_stars_repo_name": "leonfoks/coretran", "max_stars_repo_head_hexsha": "bf998d4353badc91d3a12d23c78781c8377b9578", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# Defines different storages for zarr arrays. Currently only regular files (DirectoryStore)
# and Dictionaries are supported
abstract type AbstractStore end
#Define the interface
"""
storagesize(d::AbstractStore)
This function shall return the size of all data files in a store.
"""
function storagesize end
"""
... | {"hexsha": "5b56bbc0d119b3e39c9eafd0052a5463549d6dd7", "size": 2835, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Storage/Storage.jl", "max_stars_repo_name": "manics/Zarr.jl", "max_stars_repo_head_hexsha": "a3662f17c8d7f50a4b8bf2961e18865409cee85e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#!/usr/bin/env python2.7
# __BEGIN_LICENSE__
#
# Copyright 2012 Stanford University. All rights reserved.
#
# __END_LICENSE__
# calibrate.py
#
# Usage: calibrate.py <calibration_dir>
#
# This script re-runs the calibration from a set of calibration images
# captured by the uScope GUI. This script is mostly useful f... | {"hexsha": "3c982e130f2d0483d0cc8229e5d9c72a1cee2d6c", "size": 15373, "ext": "py", "lang": "Python", "max_stars_repo_path": "stanford_lfanalyze_v0.4/lfcalibrate.py", "max_stars_repo_name": "pauledgarson/FlyLFM-Paul", "max_stars_repo_head_hexsha": "aa7e8fcae630e7db7322219437cbc0b47d4598a7", "max_stars_repo_licenses": ["... |
import numpy as np
import scipy.stats as scipystats
import torch.nn as nn
import torch
import os
import matplotlib.pyplot as plt
import scipy.misc as sm
import cv2
import random
def get_1x(model):
b = []
name = []
b.append(model.module.Scale.conv1)
b.append(model.module.Scale.bn1)
b.append(model.m... | {"hexsha": "cc8c9e5add9d17a27da0acd58ddf824c1e6ba4f9", "size": 1277, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/get.py", "max_stars_repo_name": "birdman9390/MetaMaskTrack", "max_stars_repo_head_hexsha": "8d2e13fbf31f69008f2d02724e71ba7d87aefbbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
'''
Jake Elkins built this boi
spacecraft attitude control simulator, built in the OpenAI gym format for easy interface with popular RL libraries.
this one is continuous control.
'''
import gym
from gym import spaces, logger
import numpy as np
from numba import jit
class AttitudeControlEnv(gym.Env):
# ---- t... | {"hexsha": "30eed94a41bcfb66078454a059b90873081ba10d", "size": 9190, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/ADCS_gym_cont.py", "max_stars_repo_name": "jakeelkins/rl-attitude-control", "max_stars_repo_head_hexsha": "3f268f96fecf5f1b3194b2927e0c494ce731adf6", "max_stars_repo_licenses": ["MIT"], "max_... |
#####
### This is the specialized code I wrote to simplify the similarity
### data. There is otherwise too much variation to find patterns.
### We use binning to reduce values to categorical values.
### For most of the analysis we simply reduce it to whole
### percents.
#####
using DataFrames, DataArrays
"""
find... | {"hexsha": "f44f63274fb4b115b700acec7c7e74045481fb85", "size": 3836, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "DataFrameBinning.jl", "max_stars_repo_name": "Nectarineimp/PlayerSessionGap.jl", "max_stars_repo_head_hexsha": "eae287801b3743a7546d9ae73fdea0d520b37e2d", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma finite_deriv: "finite (deriv s) = (\<exists>m. f [s] m = [])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite (deriv s) = (\<exists>m. f [s] m = [])
[PROOF STEP]
apply(rule)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. finite (deriv s) \<Longrightarrow> \<exists>m. f [s] m = []
2. \<ex... | {"llama_tokens": 5583, "file": "Verified-Prover_Prover", "length": 39} |
[STATEMENT]
lemma ord_one[simp]:
"ord \<one> = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ord \<one> = 0
[PROOF STEP]
using Zp.nonzero_one_closed local.frac_one ord_of_frac
[PROOF STATE]
proof (prove)
using this:
\<one>\<^bsub>Z\<^sub>p\<^esub> \<in> nonzero Z\<^sub>p
?a \<in> nonzero Z\<^sub>p \<Longrightarr... | {"llama_tokens": 253, "file": "Padic_Field_Padic_Fields", "length": 2} |
!** Copyright (c) 1989, NVIDIA CORPORATION. All rights reserved.
!**
!** Licensed under the Apache License, Version 2.0 (the "License");
!** you may not use this file except in compliance with the License.
!** You may obtain a copy of the License at
!**
!** http://www.apache.org/licenses/LICENSE-2.0
!**
!** Unless... | {"hexsha": "ca29461f8f6451615b4a04b31434b80f9f0d6eb2", "size": 2483, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/f90_correct/src/mmulR4mxv_t.f90", "max_stars_repo_name": "kammerdienerb/flang", "max_stars_repo_head_hexsha": "8cc4a02b94713750f09fe6b756d33daced0b4a74", "max_stars_repo_licenses": ["Apache... |
#######################################################
# #
# This file is test #
# maybe contains wrong logical operation #
# ignore this file #
# ... | {"hexsha": "d5c27fb833c66f2a67bb953d2bde1b8ff3d6f3fb", "size": 1587, "ext": "py", "lang": "Python", "max_stars_repo_path": "Q10/Q10_test.py", "max_stars_repo_name": "AliRezaBeigy/MultiMediaCourse", "max_stars_repo_head_hexsha": "069e8e438b273c9dbc093be0badb02c3d9b50d72", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
Copyright (c) 2019 Microsoft Corporation. All rights reserved.
MIT License
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,... | {"hexsha": "61c4e965091bbebd1e7b007a0c199c0c90d56247", "size": 17916, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/sr_dataset.py", "max_stars_repo_name": "gaochangfeng/pykaldi2", "max_stars_repo_head_hexsha": "5e988e5968aa9a5867f8179e6c53ea715ac46bdc", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <iostream>
#include "HexGridPrint.hpp"
#include <boost/lexical_cast.hpp>
HexGridPrint::HexGridPrint(int i_width,int i_height,
bool i_ULStart,bool i_LowShort):
m_width(i_width),
m_height(i_height),
m_ULStart(i_ULStart),
m_LowShort(i_LowShort),
m_grid(2+5*i_width,1+4*i_heigh... | {"hexsha": "95f9890ebf5f517bb7c9498d0a172d3579a4011c", "size": 2069, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "VideoGameAssistants/PuzzlePirates/Alchemy/HexGridPrint.cpp", "max_stars_repo_name": "chiendarrendor/AlbertsMisc", "max_stars_repo_head_hexsha": "f017b29f65d1d47eb22db66dff0b6d2145794fc8", "max_stars... |
[STATEMENT]
theorem homeomorphic_monotone_image_interval:
fixes f :: "real \<Rightarrow> 'a::{real_normed_vector,complete_space}"
assumes cont_f: "continuous_on {0..1} f"
and conn: "\<And>y. connected ({0..1} \<inter> f -` {y})"
and f_1not0: "f 1 \<noteq> f 0"
shows "(f ` {0..1}) homeomorphic {0..1:... | {"llama_tokens": 239616, "file": null, "length": 1419} |
import numpy as np
import schnell as snl
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font', **{'family': 'sans-serif',
'sans-serif': ['Helvetica']})
rc('text', usetex=True)
t_obs = 1
f_ref = 63
nside = 64
obs_time = t_obs*365*24*3600.
freqs = np.linspace(10., 1010., 101)
dets = [snl.Gr... | {"hexsha": "1baf13c33c8af2896e5c701f2bcbe93cba9095ab", "size": 1740, "ext": "py", "lang": "Python", "max_stars_repo_path": "plots/Nell_alpha.py", "max_stars_repo_name": "damonge/SNELL", "max_stars_repo_head_hexsha": "4bb276225fce8f535619d0f2133a19f3c42aa44f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
import numpy as np
minX = 206
maxX = 250
minY = -57
maxY = -105
distance = 0
speed = []
xarray = []
heightarray = []
def partOne():
j = abs(maxY)-1
tempheight = []
height = 0
count = 0
while height > maxY:
height += (j + count * -1)
count += 1
tempheight.append(height)
print(max(tempheight), 'is the max h... | {"hexsha": "fadf725325784121bc477971df671a8103dac98b", "size": 1092, "ext": "py", "lang": "Python", "max_stars_repo_path": "Day17.py", "max_stars_repo_name": "SheepiCagio/Advent-of-Code-2021", "max_stars_repo_head_hexsha": "52f0035da2cb258810d8947cbf56b51b65a9fe8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pyspark.sql.functions as F
#import spark_object_storage_demo_python.ibm_cos_helper as ibm_cos
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from s... | {"hexsha": "f69701f0b77dcb6cc80016a2a94007b74fb8c5d1", "size": 4737, "ext": "py", "lang": "Python", "max_stars_repo_path": "spark_object_storage_demo_python/spark_object_storage_demo_python/mission.py", "max_stars_repo_name": "nicolas2lee/Big-data-architecture", "max_stars_repo_head_hexsha": "45379068398ec0a8e4208436b9... |
import chainer
import h5py
import numpy as np
import pandas as pd
class HDF5VideoDataset(chainer.dataset.DatasetMixin):
def __init__(self, n_frames, h5path, config_path, img_size,
label=False, stride=1, xmargin=0, xflip=False):
self.n_frames = n_frames
self.h5path = h5path
... | {"hexsha": "5687e271080c43069593263ca00bf9b75e226260", "size": 2463, "ext": "py", "lang": "Python", "max_stars_repo_path": "tgan2/datasets/h5video.py", "max_stars_repo_name": "wilson1yan/tgan2", "max_stars_repo_head_hexsha": "5373136cff7af4241c9d0b1bac1357f08a509d28", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from __future__ import absolute_import
from __future__ import print_function
import sys
import glob
import time
import numpy as np
import pandas as pd
import os.path
import time
import datetime
import re
from keras.preprocessing import sequence
from keras.optimizers import SGD, RMSprop, Adagrad
from keras.utils impor... | {"hexsha": "2ceb79540e687b214199373bce7ea13273c8c7be", "size": 51403, "ext": "py", "lang": "Python", "max_stars_repo_path": "ufcnn-keras/models/UFCNN1_REPO_V16_TESTMODE.py", "max_stars_repo_name": "mikimaus78/ml_monorepo", "max_stars_repo_head_hexsha": "b2c2627ff0e86e27f6829170d0dac168d8e5783b", "max_stars_repo_license... |
using BenchmarkTools, AssociatedLegendrePolynomials, Markdown
dotune = "--tune" in ARGS
saveres = "--save" in ARGS
const PARAMS_PATH = joinpath(dirname(@__FILE__), "params.json")
const SUITE = BenchmarkGroup()
const ASSERTS = BenchmarkGroup()
const LMAX = 700
const MMAX = 350
const NORMS = (LegendreUnitNorm(),
... | {"hexsha": "d97308d2a6aedc9af73a924cef837e69136c93bd", "size": 4358, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "bench/benchmark.jl", "max_stars_repo_name": "jmert/LegendrePolynomials.jl", "max_stars_repo_head_hexsha": "b0df887a93570e176e8521faca3d8ea7a233d10b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
This script/module includes the functions to produce the noise estimate used to
determine the best aperture method to use. This function is used in getPeriodograms.py.
This script includes a way to estimate the amplitude of a light curve signal;
but is depracated since it did not yield good results in determining ... | {"hexsha": "12635436513e1b24a6b716d770e2ae88f04411b5", "size": 4766, "ext": "py", "lang": "Python", "max_stars_repo_path": "LCFeatureExtraction.py", "max_stars_repo_name": "AstroJosePC/TESSDataExploration", "max_stars_repo_head_hexsha": "3bb15d555ce9465a1ce4becd4ef62d2bf091d68d", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
# a = np.array([1, 2, 3]);
# print(a)
# b = a * 2
# print(b)
# b = a / 255
# print(b)
# c = np.max(a)
# print(c)
# b = np.arange(10);
# print(b)
# ndim:返回数组的维数
a = np.arange(24);
print(a)
print(a.ndim) # 1
# numpy.reshape: 在不改变数据的条件下修改形状
b = a.reshape(2, 4, 3);
print(b)
print(b.ndim) # 3
| {"hexsha": "7677693c963a1cabd81aa5928e9020b4152f1194", "size": 313, "ext": "py", "lang": "Python", "max_stars_repo_path": "aura/AI Engineer/course2_20191117/a1_python_lib/numpy/test.py", "max_stars_repo_name": "linksdl/futuretec-project-coursera_cerficates", "max_stars_repo_head_hexsha": "278a533501b702abd90ac3124739d3... |
# Copyright (c) 2016-present, Facebook, 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... | {"hexsha": "c6500e4e77899af0ffca008db8e0d4e712bf3177", "size": 1621, "ext": "py", "lang": "Python", "max_stars_repo_path": "caffe2/python/test_util.py", "max_stars_repo_name": "KevinKecc/caffe2", "max_stars_repo_head_hexsha": "a2b6c6e2f0686358a84277df65e9489fb7d9ddb2", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from linear_regression import Linear_regression
def Call_myLRmodel(data):
# add ones column
data.insert(0, 'Ones', 1)
# set X (training data) and y (target variable)
cols = data.shape[1]
X = data.iloc[:,0:cols-1]
y = data.... | {"hexsha": "b122d2daee4bc8519b137b6b9dad3f875f082d59", "size": 1992, "ext": "py", "lang": "Python", "max_stars_repo_path": "practice_ML/Linear_regression/multi_features.py", "max_stars_repo_name": "ives-kwy/run_ml", "max_stars_repo_head_hexsha": "17fd549bb28a731102dae1f12e2f273f314417ab", "max_stars_repo_licenses": ["M... |
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