text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
Casey likes Willy Wonka and the Chocolate Factory and Paper Clips paperclips. If he wears a fake moustache, he resembles Kurt Vonnegut.
| {"hexsha": "d48662727fe37ce0d2b85c60e434a42acbad39f2", "size": 136, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/CaseyMcGrath.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import unittest
import numpy as np
from stock_data_analysis_module.indicators import stochastic_oscillator
class StochasticOscillatorTestCase(unittest.TestCase):
def __init__(self, *args):
super().__init__(*args)
self._empty_sequence = []
self._empty_ndarray = np.zeros((0,))... | {"hexsha": "d17b70cbb507f68518c8f02038fe11a806828fb0", "size": 1110, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/test/stock_data_analysis_module/indicators/test_stochastic_oscillator.py", "max_stars_repo_name": "Freitacr/ML-StockAnalysisProject", "max_stars_repo_head_hexsha": "37411c1204ecf69040ba2a16580... |
import os
import pickle
import numpy as np
from numpy.testing import assert_allclose
import pandas as pd
import pytest
from statsmodels.tsa.seasonal import STL
cur_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(cur_dir, "results", "stl_test_results.csv")
results = pd.read_csv(file_path)
re... | {"hexsha": "7321e0e781a6de50798fb5053cb80a64863523bc", "size": 11117, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/statsmodels/tsa/tests/test_stl.py", "max_stars_repo_name": "EkremBayar/bayar", "max_stars_repo_head_hexsha": "aad1a32044da671d0b4f11908416044753360b39", "max_stars_repo_lic... |
from torch.utils import data
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import json
import train_weak_supervision.config as config
from src.utils.data_manipulation import resize, resize_generated, normalize_mean_variance
from src.utils.data_manipulation import generate_affinity, generate_t... | {"hexsha": "ae96b044a70f2c70a98e38fece94c94316307c10", "size": 7570, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_weak_supervision/dataloader.py", "max_stars_repo_name": "ds-brx/CRAFT-Remade", "max_stars_repo_head_hexsha": "1d5d990ebdca44582cd5271f13c46a27b79e0249", "max_stars_repo_licenses": ["MIT"], "... |
# Adapted from: https://gitlab.tubit.tu-berlin.de/rsim/bigearthnet-19-models/blob/93c3d398341a5ccc2c4f61d64e702901cd9340ab/prep_splits_BigEarthNet-19.py
import argparse
import os
import csv
import json
import pathlib
import gdal
import tensorflow as tf
import numpy as np
# Spectral band names to read related GeoTIFF... | {"hexsha": "20b2ef7f354fd92b3dd6a183a6672f5af3c97624", "size": 4881, "ext": "py", "lang": "Python", "max_stars_repo_path": "prepare_data.py", "max_stars_repo_name": "tommygod3/fyp-scripts", "max_stars_repo_head_hexsha": "603e4f5025f297c2242f23b1ed56991606dff31d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# k-means
## scikit-learn.cluster.kmeans
# hierarchical clustering analysis "EMSC" (Extended Multiplicative Scattering Correction)
## https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering
## https://github.com/RPCausin/EMSC/blob/master/EMSC.py: (Bassan, Konevskikh)
import enum
from typing impo... | {"hexsha": "6d534a1b0372ba6d2aa5637623ab600c8b9dc442", "size": 1636, "ext": "py", "lang": "Python", "max_stars_repo_path": "xicam/spectral/operations/clustering.py", "max_stars_repo_name": "lchen23/Xi-cam.spectral", "max_stars_repo_head_hexsha": "db3046f269443a58a6a4958f3092bfad8b71dcfa", "max_stars_repo_licenses": ["B... |
### A Pluto.jl notebook ###
# v0.14.5
using Markdown
using InteractiveUtils
# ╔═╡ 774b5c68-c2e8-11eb-0c25-c124276640d6
begin
using Revise
using Pkg
Pkg.activate(tempname())
Pkg.develop(url="https://github.com/manuelbb-upb/RadialBasisFunctionModels.jl.git")
Pkg.add("Flux")
Pkg.add("MLDatasets")
using Flux, MLDa... | {"hexsha": "c61bfaf1f3573ed5a8cb513453a9857b880e5bcd", "size": 5062, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Flux_Notebook.jl", "max_stars_repo_name": "manuelbb-upb/RBFModels.jl", "max_stars_repo_head_hexsha": "d321761fd58c88a1b11f6f1cf0e82b6ab64531e8", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
class BatchIterator:
def __init__(self, inputs, labels, batch_size):
self.inputs = inputs
self.labels = labels
self.batch_size = batch_size
self.size = self.inputs.shape[0]
self.epochs = 0
self.cursor = 0
self.shuffle()
def shuffl... | {"hexsha": "6edbbbeae1dfd4b86ca991a620f56895db850f92", "size": 880, "ext": "py", "lang": "Python", "max_stars_repo_path": "batch_iterator.py", "max_stars_repo_name": "suleymanaslan/open-set-identification-gan", "max_stars_repo_head_hexsha": "8ffa37790134394aaac9ed87d30d341c02f7228a", "max_stars_repo_licenses": ["MIT"],... |
import random
from collections import defaultdict
import copy
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from gensim.models.keyedvectors import Vocab
from six import iteritems
from sklearn.metrics import auc, f1_score, precision_recall_curve, roc_auc_sco... | {"hexsha": "72a82d2509758280dba01e6327e17775c4f9766d", "size": 4378, "ext": "py", "lang": "Python", "max_stars_repo_path": "cogdl/tasks/multiplex_link_prediction.py", "max_stars_repo_name": "awesome-archive/cogdl", "max_stars_repo_head_hexsha": "0a354eaaaf851e7218197508e7e85a81d3fb5753", "max_stars_repo_licenses": ["MI... |
library(rstan)
library(loo)
rstan_options(auto_write = TRUE);
options(mc.cores = parallel::detectCores());
plotProfileFit <- function(fit, data, targetSco, numSamples = 10) {
true_value = extract(fit,'true_value')$true_value;
samplesToPlot = true_value[sample(1:(dim(true_value)[1]),numSamples),];
#Add the ... | {"hexsha": "89e226c1ac890a3a1f773e1a8dde0cca623c391a", "size": 3072, "ext": "r", "lang": "R", "max_stars_repo_path": "stan-experiments/interpolate-funcs.r", "max_stars_repo_name": "cas-bioinf/genexpi-stan", "max_stars_repo_head_hexsha": "1164ff1c44ed967574aace8f629e5315fa70e18b", "max_stars_repo_licenses": ["BSD-3-Clau... |
module TimePetriNets
using Requires
include("base.jl")
include("Monoflop.jl")
function __init__()
# Optional dependency for makie-based plotting
@require GraphMakie="1ecd5474-83a3-4783-bb4f-06765db800d2" begin
@require GLMakie="e9467ef8-e4e7-5192-8a1a-b1aee30e663a" begin
include("plotting... | {"hexsha": "96136384a378957f49ce863852d8169a2397de46", "size": 360, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TimePetriNets/TimePetriNets.jl", "max_stars_repo_name": "jleugeri/TimedAutomata.jl", "max_stars_repo_head_hexsha": "97305371cc4fd150069ea5634663e1b30e3a8987", "max_stars_repo_licenses": ["MIT"],... |
// Copyright Louis Dionne 2013-2016
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
#ifndef TEST_SUPPORT_SEQ_HPP
#define TEST_SUPPORT_SEQ_HPP
#include <boost/hana/fwd/at.hpp>
#include <boost/hana/fwd/concept/sequen... | {"hexsha": "2819161dfb541349753298fe92722f3a471a001c", "size": 3832, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "thirdparty-cpp/boost_1_62_0/libs/hana/test/_include/support/seq.hpp", "max_stars_repo_name": "nxplatform/nx-mobile", "max_stars_repo_head_hexsha": "0dc174c893f2667377cb2ef7e5ffeb212fa8b3e5", "max_st... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import sys
if sys.version_info >= (3,0):
xrange = range
class MultiFieldPlotter:
"""
This class tries to make it easier to view functions of mor... | {"hexsha": "7ec4f2ee284e4b8a0ea801e388162c3a2eb0f304", "size": 6894, "ext": "py", "lang": "Python", "max_stars_repo_path": "cosmoTransitions/multi_field_plotting.py", "max_stars_repo_name": "ycwu1030/CosmoTransitions", "max_stars_repo_head_hexsha": "dabf3fe02c05d13458571e84398a148aad5aec4c", "max_stars_repo_licenses": ... |
/*
Copyright [2017-2020] [IBM Corporation]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in wri... | {"hexsha": "d9d05113de278f6de927e44fac0e8e261cfcdde4", "size": 26713, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/server/ado/src/ado.cpp", "max_stars_repo_name": "omriarad/mcas", "max_stars_repo_head_hexsha": "f47aab12754c91ebd75b0e1881c8a7cc7aa81278", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
/*++
BDcpp -- Simple Bjontegaard Delta metric implementation for C++.
MIT License
Copyright (c) 2022 Tim Bruylants
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... | {"hexsha": "dba67f1114390e57ae37931f331e903acb2035fe", "size": 5301, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "bdcpp/bdcpp.cpp", "max_stars_repo_name": "tbr/bjontegaard_cpp", "max_stars_repo_head_hexsha": "d06302497d8af0734d69ce93590acab9a4998817", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
#########################################################################################
# WaveformQSRCNN.py: QSRCNN for G711/ADPCM/AMR/EVS using using Waveform features
# Author: Huijun Liu
# Time: 10.05.2017
# Location: TU Braunschweig IfN
#######################################################################... | {"hexsha": "bdf6dbd1774eb6ae48319521169e3b1a485ff2cb", "size": 13097, "ext": "py", "lang": "Python", "max_stars_repo_path": "CepstralCNN/WaveformQSRCNN.py", "max_stars_repo_name": "ansleliu/ConvolutionaNeuralNetworksToEnhanceCodedSpeech", "max_stars_repo_head_hexsha": "2f0852ef2d97338a8cf42fe7e20231f38c0613de", "max_st... |
# import numpy as np
from keras import backend as K
def mean_squared_error(y_true, y_pred):
return K.mean(K.square(y_pred - y_true))
def root_mean_square_error(y_true, y_pred):
return mean_squared_error(y_true, y_pred) ** 0.5
def rmse(y_true, y_pred):
return mean_squared_error(y_true, y_pred) ** 0.5
# a... | {"hexsha": "82438b7d262769522ea2122a7ecc377871c0b076", "size": 397, "ext": "py", "lang": "Python", "max_stars_repo_path": "star/metrics.py", "max_stars_repo_name": "hongnianwang/STAR", "max_stars_repo_head_hexsha": "11cad8a01c6661bbaec7da511a1c9cb960896b00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "ma... |
import numpy as np
NG_EXACT = 'exact'
NG_BD = 'block_diagonal'
NG_BTD = 'block_tri_diagonal'
NG_KFAC = 'kfac'
__all__ = [
'batch_analytic_kernel',
'analytic_kernel'
]
def batch_analytic_kernel(x1, x2, w_var, ng_type, batch_size):
N1 = x1.shape[0]
N2 = x2.shape[0]
assert N1 % batch_size == 0 and N2 % batc... | {"hexsha": "393197a6c411caf67487c794065becd056182f7c", "size": 2321, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy-based/analytical.py", "max_stars_repo_name": "kazukiosawa/ngd_in_wide_nn", "max_stars_repo_head_hexsha": "1da1a708f8486c16010856d4b43c640b86a14893", "max_stars_repo_licenses": ["MIT"], "max_... |
# -*- coding:utf-8 -*-
__author__ = 'boredbird'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from datetime import datetime
from sklearn.svm import l1_min_c
from woe.eval import compute_ks
import pickle
import time
"""
Search for optimal hyp... | {"hexsha": "90bb7085359c8182866971e431350e5705f6665b", "size": 9955, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/woe/GridSearch.py", "max_stars_repo_name": "Jie-Yuan/woe", "max_stars_repo_head_hexsha": "335e9ec2a521d3bbccb0ad5d915128119e4d0ca6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
POI_grid
--------
POI: probability of instantiation
"""
import numpy as np
import os
from .constants import ROW, COL, create_deepcopy
import copy
from multigrids import TemporalMultiGrid
class POIGrid (TemporalMultiGrid):
""" Class doc """
def __init__ (self, *args, **kwargs):
"""This class r... | {"hexsha": "38c77c920c890cca2c4c7892675a5fe84d8d6244", "size": 2306, "ext": "py", "lang": "Python", "max_stars_repo_path": "atm/grids/poi_grid.py", "max_stars_repo_name": "gina-alaska/arctic_thermokarst_model", "max_stars_repo_head_hexsha": "7a3dbedb72b133670bb6e476fc3f5788bbcdbca4", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma term_variants_pred_param:
assumes "term_variants_pred P t s"
and fg: "f = g \<or> g \<in> set (P f)"
shows "term_variants_pred P (Fun f (S@t#T)) (Fun g (S@s#T))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. term_variants_pred P (Fun f (S @ t # T)) (Fun g (S @ s # T))
[PROOF STEP]
proof -
... | {"llama_tokens": 994, "file": "Automated_Stateful_Protocol_Verification_Term_Variants", "length": 10} |
"""
Module to train DBN on MNIST.
"""
from os import path
from pylearn2.config import yaml_parse
from pylearn2.utils import serial
from theano import config
#config.exception_verbosity='high'
#config.optimizer='fast_compile'
#config.mode='DEBUG_MODE'
def train_yaml(yaml_file):
train = yaml_parse.load(yaml_file)... | {"hexsha": "8781f41dac482d1fe15c07dd1168f16135f135a8", "size": 1702, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylearn2/scripts/tutorials/dbn_demo/train.py", "max_stars_repo_name": "rdevon/pylearn2", "max_stars_repo_head_hexsha": "f7b9a6ea0e2498176b47202f5bb83aec4976e1dd", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma to_list_tree_single: "v \<in> verts to_list_tree \<Longrightarrow> \<exists>x. v = [x] \<and> x \<in> verts T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. v \<in> verts to_list_tree \<Longrightarrow> \<exists>x. v = [x] \<and> x \<in> verts T
[PROOF STEP]
unfolding to_list_tree_def
[PROOF STATE]... | {"llama_tokens": 248, "file": "Query_Optimization_Directed_Tree_Additions", "length": 2} |
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.metrics import mean_squared_error
from datetime import datetime, timedelta
import os
import time
from config import *
from dataset.wrapped_dataloader import *
from model.seq2seq.encoder import Encoder
from model.seq2seq.deco... | {"hexsha": "1502725f78e357fbdf2c1b4dab0d2b459576f80b", "size": 5457, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "Aquarium1222/Electricity-Forecasting", "max_stars_repo_head_hexsha": "9f945d3fd8006e5d77da08ff7861577965109ec8", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma fixp_strong_induct_uc:
fixes F :: "'c \<Rightarrow> 'c"
and U :: "'c \<Rightarrow> 'b \<Rightarrow> 'a"
and C :: "('b \<Rightarrow> 'a) \<Rightarrow> 'c"
and P :: "('b \<Rightarrow> 'a) \<Rightarrow> bool"
assumes mono: "\<And>x. mono_body (\<lambda>f. U (F (C f)) x)"
and eq: "f \<... | {"llama_tokens": 796, "file": null, "length": 6} |
#!/usr/bin/python
from __future__ import division, print_function
import h5py
from struct import pack
import sys
import os
import numpy as np
from glob import glob
from argparse import ArgumentParser
def convert_ptype(inbase, outbase, ptype='PartType1', overwrite=False, verbose=False):
files = glob(inbase+'*')
... | {"hexsha": "0a3881472cbc16385c6b7e6b0853d0f31af12128", "size": 7358, "ext": "py", "lang": "Python", "max_stars_repo_path": "sgklibs/convert_ptype_to_gbin.py", "max_stars_repo_name": "sheagk/sgklibs", "max_stars_repo_head_hexsha": "47e99f7f2e24dfabff039f005847e702a12138b1", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import pickle
class Expert_data(object):
def __init__(self, fpath):
self.load_data(fpath)
self.pointer = 0
def load_data(self, fpath):
with open(fpath, 'rb') as f:
data = pickle.load(f)
self.obs = data['observations']
self.acts = data['actions']
self.acts = self.a... | {"hexsha": "c1abfb89bcc71c64910f49600767109b349007e2", "size": 1418, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw1/data_set.py", "max_stars_repo_name": "AnxietyYoungPoet/CS294-122", "max_stars_repo_head_hexsha": "8e813c62fc1ef0bac18f97f3045d60bec4d02b72", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
~~~ REDIMENSIONALIZE THE SIMULATION AND DATA FILES ~~~
Re dimensionalizing and plotting the CQ Data (sims AND experimental)
will be done in a few steps
1. Read in the 3 experimental data files
2. Read in the 3*4 = 12 simulation data files
3. Dimensionalize P, M, wp, wm, and sb for both the data and the simulatio... | {"hexsha": "0fbe33565366a50b20fef92ae8a562438aec352e", "size": 5520, "ext": "py", "lang": "Python", "max_stars_repo_path": "Old Python Code/ReDim.py", "max_stars_repo_name": "CRZaug/NonlinearWaves", "max_stars_repo_head_hexsha": "2adfc2cc5e0c18576c6b73420a913ef1ce23000d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import cv2
from google.colab.patches import cv2_imshow
from fmd.mark_dataset.util import draw_marks
import numpy as np
from face_detector.detector import Detector
import tensorflow as tf
class predictor():
def __init__(self, detector_face_model_path, cnn_model_path, threshold=0.7):
self.detector_face_model = Det... | {"hexsha": "d4a285bcfb939208141126913a0f7ed0572efee8", "size": 1771, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "keshav47/cnn-facial-landmark", "max_stars_repo_head_hexsha": "2c4011db2917428ebebf70d7fc9ad59c89002bfc", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .losses import *
from .modules.layers import *
from .modules.context_module import *
from .modules.attention_module import *
from .modules.decoder_module import *
from .backbones.Res2Net_v1b import res2net50_v1b_26w_4s
class P... | {"hexsha": "5d9181ef47d024e14ff501b336d0f6339187288f", "size": 2275, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/PraNet.py", "max_stars_repo_name": "Inovasyon-ve-Analiz/uacanet", "max_stars_repo_head_hexsha": "4f6567840360ece53868888acd72a16de8279dc2", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python3
# Copyright 2021 by Julian Nubert, Robotic Systems Lab, ETH Zurich.
# All rights reserved.
# This file is released under the "BSD-3-Clause License".
# Please see the LICENSE file that has been included as part of this package.
import os
import glob
import numpy as np
import torch
import ros_uti... | {"hexsha": "12c01a21d331579da99fcc7aef62de83cc7ef138", "size": 1574, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/rosbag_scans.py", "max_stars_repo_name": "leggedrobotics/DeLORA", "max_stars_repo_head_hexsha": "909948d63a9517e6dd54bedcf099f6b39ded2cb4", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import tensorflow as tf
import numpy as np
from models import TransWeight
from tests import TestBase
class TransWeightTest(TestBase):
"""
This class tests the functionality of the TransWeight model.
This test suite can be ran with:
python -m unittest -q tests.TransWeightTest
"""
def setUp... | {"hexsha": "d4cff420573d60bfd9539ab3e609e54dbf138f17", "size": 3974, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_transweight.py", "max_stars_repo_name": "larsvansoest/commix", "max_stars_repo_head_hexsha": "c5629a94d8657e67919fdbb42da99026e85023e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 24 14:28:26 2019
@author: Emma
"""
import numpy as np
import os,sys
sys.path.append(os.pardir)
from dataset.mnist import load_mnist
import matplotlib.pylab as plt
def cross_entropy_error(y,t):
if y.ndim==1:
t=t.reshape(1,t.size())
... | {"hexsha": "b3d81f42886e6f8d04dca76a6d13968994da7238", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch04/practice.py", "max_stars_repo_name": "py0922/deepLearn", "max_stars_repo_head_hexsha": "32d0020ac98c1c9e65acd943087cec6167808c14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
using LinearAlgebra, Zygote, Optim, IterativeSolvers, Arpack
using CSV, DataFrames
include("model.jl")
function data()
abalone = CSV.File(open("warpedgp/abalone.csv"))
abalone = DataFrame(abalone)
X = Matrix(Matrix(abalone[1:200,2:8])')
y = Vector{Float64}(abalone[1:200, 9])
f = Linear()
g = I... | {"hexsha": "6a7cfd23b8a7c04d1bacebcb47c30d6df3c3b342", "size": 1911, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "BayesTransformedGaussian/src/various_mle_things/warpedgp/run.jl", "max_stars_repo_name": "dbindel/btg", "max_stars_repo_head_hexsha": "83616be1d1c4d80f385fa95cef38753f39a3a4c3", "max_stars_repo_lic... |
[STATEMENT]
lemma word_shift_nonzero:
"\<lbrakk> (x::'a::len word) \<le> 2 ^ m; m + n < LENGTH('a::len); x \<noteq> 0\<rbrakk>
\<Longrightarrow> x << n \<noteq> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x \<le> 2 ^ m; m + n < LENGTH('a); x \<noteq> 0\<rbrakk> \<Longrightarrow> x << n \<noteq> 0... | {"llama_tokens": 1468, "file": "Word_Lib_Word_Lemmas", "length": 10} |
"""
Copyright 2013 Steven Diamond
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software... | {"hexsha": "e1c9998d7fb25319beef04ee215b062a89237814", "size": 2864, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvxpy/atoms/sum_largest.py", "max_stars_repo_name": "jasondark/cvxpy", "max_stars_repo_head_hexsha": "56aaa01b0e9d98ae5a91a923708129a7b37a6f18", "max_stars_repo_licenses": ["ECL-2.0", "Apache-2.0"... |
function psnr=compute_psnr(im1,im2)
if size(im1, 3) == 3,
im1 = rgb2ycbcr(im1);
im1 = im1(:, :, 1);
end
if size(im2, 3) == 3,
im2 = rgb2ycbcr(im2);
im2 = im2(:, :, 1);
end
imdff = double(im1) - double(im2);
imdff = imdff(:);
rmse = sqrt(mean(imdff.^2));
psnr = 20*log10(255/rmse); | {"author": "tyshiwo", "repo": "DRRN_CVPR17", "sha": "cafe98bc73997c10947911de74279d63cb786b8a", "save_path": "github-repos/MATLAB/tyshiwo-DRRN_CVPR17", "path": "github-repos/MATLAB/tyshiwo-DRRN_CVPR17/DRRN_CVPR17-cafe98bc73997c10947911de74279d63cb786b8a/test/evaluation_func/compute_psnr.m"} |
import calendar
from datetime import datetime as dt
from datetime import timedelta
import logging
import time
from time import sleep
import sys
import numpy as np
import requests
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
LOG = logging.getLogger("main")
def utcnow():
... | {"hexsha": "6dba61caafbf98e65ae5933dda72eb00d3b3601a", "size": 7645, "ext": "py", "lang": "Python", "max_stars_repo_path": "indiana.py", "max_stars_repo_name": "mildbyte/indiana", "max_stars_repo_head_hexsha": "fa35d4f1a08f92b77b698105f92054086ee11883", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
import os
import pickle
import uuid
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
from keras import backend as K
from keras.datasets import mnist
from keras.layers import (Activation, Conv2D, Dense, Dropout, Flatten,
MaxPooling2D)
from keras.models import Sequenti... | {"hexsha": "6b243c5a16c2dcafc6e4356193c3a26ca651b092", "size": 2740, "ext": "py", "lang": "Python", "max_stars_repo_path": "predictors/from_webcam.py", "max_stars_repo_name": "marmightygood/image_classifier", "max_stars_repo_head_hexsha": "8dfc94efdb15206a45d6956e115e5291c2485b15", "max_stars_repo_licenses": ["Apache-2... |
export Periodic , Bloch, DirichletNeumann , NeumannDirichlet , NeumannNeumann , DirichletDirichlet
abstract type BCType end
struct Periodic <: BCType end
struct Bloch <: BCType end
struct DirichletNeumann <: BCType end
struct NeumannDirichlet <: BCType end
struct NeumannNeumann <: BCType end
struct DirichletDirichlet ... | {"hexsha": "846d79b3b3212c47caa5e3951a55f2a10231e441", "size": 333, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Interface/BC.jl", "max_stars_repo_name": "MKAbdElrahman/Photon.jl", "max_stars_repo_head_hexsha": "f75c0b572233094c8a049d3bf572489b6d5d23d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import math, cmath
import numpy as np
from openmdao.api import ImplicitComponent, Group
from openmdao.api import DirectSolver, BoundsEnforceLS, NewtonSolver
from pyLF.LF_elements.generator import ACgenerator, DCgenerator
from pyLF.LF_elements.load import ACload, DCload
class InverterCalcs(ImplicitComponent):
d... | {"hexsha": "cd2591ad8d4d4bc12fee83408543dabdb8696b22", "size": 13947, "ext": "py", "lang": "Python", "max_stars_repo_path": "zappy/LF_elements/inverter.py", "max_stars_repo_name": "OpenMDAO/zappy", "max_stars_repo_head_hexsha": "2c72048b4c4e0ce0ae83221e4ee5788978254340", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
# This Source Code Form is subject to the terms of the MIT
# License. If a copy of the same was not distributed with this
# file, You can obtain one at
# https://github.com/akhilpandey95/scholarlyimpact/blob/master/LICENSE.
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.preprocessing import ... | {"hexsha": "686ffc27dec9d30aac5f98d02724579efd281ae0", "size": 4187, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data.py", "max_stars_repo_name": "akhilpandey95/scholarlyimpact", "max_stars_repo_head_hexsha": "215ae832c90f0564fa0301e4c3f1c99525617625", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# using StaticArrays: later package all types as static/mutable fixed size arrays/matrices
"""
Definition of abstract spatial types to be used across the package
"""
# todo: make types parametrized: {N(dims), T(type)}
# todo: look for packages to depend on: GeometryTypes
# todo: quaternions?
abstract type AbstractPoin... | {"hexsha": "43546a92d81109967509aeee3f77de1a5655b455", "size": 780, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/spatialmaths/spatial_types.jl", "max_stars_repo_name": "pranshumalik14/RoboticsToolkit.jl", "max_stars_repo_head_hexsha": "652daaafc8f5ab27e2817252007a28ff3427a311", "max_stars_repo_licenses": [... |
@testset "1.1.2.3 (a+b x^2)^p (c+d x^2)^q" begin
(a, b, c, d, m, p, q, x, ) = @variables a b c d m p q x
#= ::Package:: =#
#= ::Title:: =#
#=Integrands*of*the*form*(a+b*x^2)^p*(c+d*x^2)^q=#
#= ::Section::Closed:: =#
#=Integrands*of*the*form*(a+b*x^2)^p*(c+d*x^2)^q=#
#= ::Subsubsection:... | {"hexsha": "40bb70d08704467b2fddc7705dd36180cc356d95", "size": 126528, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/1 Algebraic functions/1.1 Binomial products/1.1.2 Quadratic/1.1.2.3 (a+b x^2)^p (c+d x^2)^q.jl", "max_stars_repo_name": "gronniger/RubiSymbolics.jl", "max_stars_repo_head_hexsha": "dd117985d... |
import numpy as np
from scipy.sparse import csr_matrix, csc_matrix, lil_matrix, save_npz, load_npz
l = [[0, 1, 2],
[3, 0, 4],
[0, 0, 0]]
csr = csr_matrix(l)
csc = csc_matrix(l)
save_npz('data/temp/csr.npz', csr)
save_npz('data/temp/csc.npz', csc)
csr_ = load_npz('data/temp/csr.npz')
print(csr_.toarray())
... | {"hexsha": "43cf33a2f2c819305357334e15d9f0fe8d75a5d4", "size": 969, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebook/scipy_sparse_save_load_npz.py", "max_stars_repo_name": "vhn0912/python-snippets", "max_stars_repo_head_hexsha": "80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038", "max_stars_repo_licenses": ["MIT... |
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
from matplotlib.colors import ListedColormap, BoundaryNorm
import functools
from math import floor
import numpy as np
from src.PathFindingAlgorithms import BFS, Dijkstra, Astar
class PathGUI():
def __init__(self, Map, pathFinderType=... | {"hexsha": "54c609fd70c2b19f6792ee5258fbe4db750ae52f", "size": 6343, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/GUI.py", "max_stars_repo_name": "Lilweavs/PathFinding", "max_stars_repo_head_hexsha": "56266ab6958473449852b9b47784daded797430d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# Import all the useful libraries
import numpy as np
import pandas as pd
import fancyimpute
from sklearn import model_selection
from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import AdaBoostClassifier # PROBABILITY
from sklearn.tree import DecisionTreeClassifier # PROBABILITY
from sklearn.... | {"hexsha": "f9469d662c8a394418e1281849c9338717fd6652", "size": 33289, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/LocalModel.py", "max_stars_repo_name": "hades208002/mdp-project", "max_stars_repo_head_hexsha": "c242a8d00412cc3772d298986977f6acc47002ee", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 19 08:15:32 2018
Computes the global extrema metrics for regridded and reference target data
@author: jeguerra
"""
import numpy as np
def computeGlobalExtremaMetrics(varS2T, varST):
Lden = (np.max(abs(varST)) - np.min(abs(varST)))
L_min... | {"hexsha": "d0819aef474400c99d08b41c868c60d12b9ce2e8", "size": 530, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/computeGlobalExtremaMetrics.py", "max_stars_repo_name": "CANGA/MIRA", "max_stars_repo_head_hexsha": "2f1214d34b884790fa8660b5208cd12495800f92", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import pickle
import numpy as np
import pandas as pd
from config.config import DATA_PATH, VECTORIZER_PATH
from src.data.preprocess_data import preprocess_data
from src.features.vectorize import split_dataset, fit_vectorizer, transform_vectorizer
from sklearn.metrics import f1_score, classification_report
from sklearn.n... | {"hexsha": "75d24f29f694fb9a2e8e15a8122ef8bbb48f33c0", "size": 1349, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/baseline.py", "max_stars_repo_name": "silencedsre/Fusemachines-AI-Training", "max_stars_repo_head_hexsha": "46ae5be0895fee66ef8a37f1da40a6d3a4bfde5a", "max_stars_repo_licenses": ["Apach... |
from skimage import io as sio
import skimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
def reverse_jet(img):
img = skimage.img_as_float(sio.imread(img))
jet = plt.cm.jet
jet._init()
lut = jet._lut[..., :3]
z = img - lut[:, None, None, :]
z *= z
d = z.sum(axis=-... | {"hexsha": "39f6f1fe5bc9be97730b53ab69c6a6fe24660b0d", "size": 796, "ext": "py", "lang": "Python", "max_stars_repo_path": "reverseJet/reverseJet.py", "max_stars_repo_name": "ajpfahnl/labUtils", "max_stars_repo_head_hexsha": "11130f08100809fb9820f6771cb0666e4908a116", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma step_4_push_big_size_ok_1: "\<lbrakk>
invar (States dir big small);
4 \<le> remaining_steps (States dir big small);
(step^^4) (States dir (Big.push x big) small) = States dir' big' small';
remaining_steps (States dir big small) + 1 \<le> 4 * size small
\<rbrakk> \<Longrightarrow> remaining_st... | {"llama_tokens": 351, "file": "Real_Time_Deque_States_Proof", "length": 1} |
using Documenter, LearnBase, LossFunctions
istravis = "TRAVIS" ∈ keys(ENV)
makedocs(
format = Documenter.HTML(assets=["assets/style.css","assets/favicon.ico"], prettyurls=istravis),
sitename = "LossFunctions.jl",
authors = "Christof Stocker, Tom Breloff, Alex Williams",
pages = [
hide("Home" =... | {"hexsha": "c3033084d7f6f172ad19ea6512b779f207585f5a", "size": 983, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "Evizero/MLModels.jl", "max_stars_repo_head_hexsha": "521ff95e5514329b7c98260df99cfde25d08d3b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 103, "ma... |
import numpy as np
import nest
import matplotlib.pyplot as plt
import pylab as pl
###################################################################################
# Parameters
###################################################################################
# General simulation parameters
dt = 0... | {"hexsha": "9eb6ddce0b01cf85243f781c8a4a27c063dae299", "size": 12389, "ext": "py", "lang": "Python", "max_stars_repo_path": "associtive_properties.py", "max_stars_repo_name": "juliavg/assembly-structural-plasticity", "max_stars_repo_head_hexsha": "d00fc61fc6fea625f1f133efbc5847e8c0cc0341", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma transpose_mat_plus: assumes wf: "mat nr nc m1" "mat nr nc m2"
shows "transpose nr (mat_plusI pl m1 m2) = mat_plusI pl (transpose nr m1) (transpose nr m2)" (is "?l = ?r")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Matrix_Legacy.transpose nr (mat_plusI pl m1 m2) = mat_plusI pl (Matrix_Legacy.tr... | {"llama_tokens": 1558, "file": "Matrix_Matrix_Legacy", "length": 8} |
from numba import jit
import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import time
# General Constants
MASS_E = 9.10938291e-31 # electron mass
MASS_P = 1.67262178e-27 # proton mass
STEF_BOLT = 5.670373e-8 # Stefan-Boltzmann constant
K_BOLT = 1.381e-23 # Boltzmann constant
C = 299792458 # sp... | {"hexsha": "398fa1ea70be0d5b50a64db1b5e7dc01ff2857b7", "size": 18716, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Star_Creator.py", "max_stars_repo_name": "iisharankov/Phys375-Stellar-Warming", "max_stars_repo_head_hexsha": "2cf8e81f11701ec4d04752cbdf27805ec187ace7", "max_stars_repo_licenses": ["MIT"], "... |
# --------------
# Importing header files
import numpy as np
# Path of the file has been stored in variable called 'path'
data = np.genfromtxt(path,delimiter=',',skip_header=1)
print(data)
#New record
new_record=[[50, 9, 4, 1, 0, 0, 40, 0]]
census = np.concatenate((data,new_record),axis=0)
print(census)... | {"hexsha": "17aea76707478ac2e9cdc5a5aa26c7d15fe1b658", "size": 1682, "ext": "py", "lang": "Python", "max_stars_repo_path": "Numpy/code.py", "max_stars_repo_name": "niketkaria/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "e46e9f902907178712c6756dbb1435daf43d65d5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#import pygame
from PIL import Image
import numpy as np
from raycast import Raycaster
from skimage.draw import line_aa
#from pygame.constants import K_w, K_a, K_d, K_s, K_SPACE
import sys
import pdb
OBJECT_MAP = {
'open': 0,
'wall': 1,
'food': 2,
'poison': 3,
'agent': 4,
'floor': 5,
'sky': ... | {"hexsha": "9526e756d0e2947eacfba88f9fca61081d035034", "size": 23766, "ext": "py", "lang": "Python", "max_stars_repo_path": "world.py", "max_stars_repo_name": "ScottJordan/waterworld", "max_stars_repo_head_hexsha": "5ad2e670939880944fc0a209c5eef153705afce6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
import numpy as np
import scipy.stats as st
def theta_init(K):
return(np.ones(K)/K)
def beta_ber_init(x,K):
n,_ = x.shape
return(np.ones((K,n))*0.5)
def beta_mean_sampleinit(x,K):
n,_ = np.shape(x)
i_sample = np.random.choice(list(range(n)),size=K, replace=False)
x_sample = x[i_sample,:]
re... | {"hexsha": "b4fbdc59a62478617b0019ab4015009495527073", "size": 622, "ext": "py", "lang": "Python", "max_stars_repo_path": "HW1/gibbs_sampling/initialization.py", "max_stars_repo_name": "DonghanHe/6701-Foundations-of-Graphical-Models", "max_stars_repo_head_hexsha": "3e9dfd7f85e4bbe4848dc1f4159e193e8193fe4a", "max_stars_... |
header {* Optimizations for Code Float *}
theory Optimize_Float
imports
"../ODE_Auxiliarities"
Optimize_Integer
begin
lemma compute_bitlen[code]: "bitlen a = (if a > 0 then log2 a + 1 else 0)"
by (simp add: bitlen_def log2_def)
lemma compute_real_of_float[code]:
"real_of_float (Float m e) = (if e \<ge> 0 then... | {"author": "Josh-Tilles", "repo": "AFP", "sha": "f4bf1d502bde2a3469d482b62c531f1c3af3e881", "save_path": "github-repos/isabelle/Josh-Tilles-AFP", "path": "github-repos/isabelle/Josh-Tilles-AFP/AFP-f4bf1d502bde2a3469d482b62c531f1c3af3e881/thys/Ordinary_Differential_Equations/Numerics/Optimize_Float.thy"} |
//---------------------------------------------------------------------------//
//!
//! \file tstUniformDistribution.cpp
//! \author Alex Robinson
//! \brief Uniform distribution unit tests.
//!
//---------------------------------------------------------------------------//
// Std Lib Includes
#include <iostream>
... | {"hexsha": "cb66497d146c80933182d4ea5e4b11a9bf0ae233", "size": 36370, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "packages/utility/distribution/test/tstUniformDistribution.cpp", "max_stars_repo_name": "lkersting/SCR-2123", "max_stars_repo_head_hexsha": "06ae3d92998664a520dc6a271809a5aeffe18f72", "max_stars_rep... |
import random
import re
from multiprocessing import Pool
from collections import UserList, defaultdict
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import torch
from rdkit import rdBase
from rdkit import Chem
# https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
def se... | {"hexsha": "a3fb30a0d2afd0850954413efb15b584e398c908", "size": 9163, "ext": "py", "lang": "Python", "max_stars_repo_path": "moses/utils.py", "max_stars_repo_name": "yair-schiff/moses", "max_stars_repo_head_hexsha": "563c364acf6091bf1781f0f98743589ce4eb4195", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "ba1689c6f913a7b1009363895f951405015b07ec", "size": 8480, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/st/probability/distribution/test_exponential.py", "max_stars_repo_name": "GuoSuiming/mindspore", "max_stars_repo_head_hexsha": "48afc4cfa53d970c0b20eedfb46e039db2a133d5", "max_stars_repo_lic... |
# -*- coding: utf-8 -*-
#
## copyright (C) 2018
# The Icecube Collaboration
#
# $Id$
#
# @version $Revision$
# @date $LastChangedDate$
# @author Hershal Pandya <hershal@udel.edu> Last changed by: $LastChangedBy$
#
from icecube import phys_services, dataclasses, icetray
import numpy as np
import tables
from icecube... | {"hexsha": "b10d728b0abce06ab54ee884e51cda372fa59c93", "size": 17767, "ext": "py", "lang": "Python", "max_stars_repo_path": "icetray_version/releases/V00-00/i3module.py", "max_stars_repo_name": "hershalpandya/airshowerclassification_llhratio_test", "max_stars_repo_head_hexsha": "a2a2ce5234c8f455fe56c332ab4fcc65008e9409... |
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE HeapTest
/*
* @author : Ionesio Junior
*/
#include <boost/test/unit_test.hpp>
#include "../BinaryHeap.hpp"
#include <cstdlib>
#include <iostream>
#include <time.h>
#include <vector>
struct setUp{
BinaryHeap<int> *heap = new BinaryHeap<int>(10);
int empty[0] = {... | {"hexsha": "6042cfb2741bb9d5300016d81413a233d3ef2c21", "size": 5551, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Data Structures/Heap/C++/Test/test.cpp", "max_stars_repo_name": "Julian-Mentasti/codezilla", "max_stars_repo_head_hexsha": "ca157d75628f68ab01d589267f26d17751a87e86", "max_stars_repo_licenses": ["MI... |
Require Import Bedrock.Platform.Thread Bedrock.Platform.Arrays8 Bedrock.Platform.MoreArrays Bedrock.Platform.Buffers Bedrock.Platform.Io.
Local Hint Extern 1 (@eq W _ _) => words.
Module Type S.
Parameters globalSched globalSock : W.
Parameter inbuf_size : nat.
Axiom inbuf_size_lower : (inbuf_size >= 2)%nat.
... | {"author": "mit-plv", "repo": "bedrock", "sha": "e3ff3c2cba9976ac4351caaabb4bf7278bb0dcbd", "save_path": "github-repos/coq/mit-plv-bedrock", "path": "github-repos/coq/mit-plv-bedrock/bedrock-e3ff3c2cba9976ac4351caaabb4bf7278bb0dcbd/Bedrock/Platform/tests/EchoServer.v"} |
[STATEMENT]
lemma register_unit_register[simp]: \<open>is_unit_register U \<Longrightarrow> register U\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. is_unit_register U \<Longrightarrow> register U
[PROOF STEP]
by (simp add: compatible_def complements_def is_unit_register_def) | {"llama_tokens": 95, "file": "Registers_Laws_Complement", "length": 1} |
from typing import List, Dict, Any, Tuple, Union, Optional
from collections import namedtuple, deque
import torch
import numpy as np
import copy
from ding.torch_utils import Adam, to_device
from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample
from ding.model import model_wrap
from ding.utils i... | {"hexsha": "e08f23900694a5cf011eab8c8c94a9f5911fa342", "size": 15813, "ext": "py", "lang": "Python", "max_stars_repo_path": "ding/policy/atoc.py", "max_stars_repo_name": "uuid0000/DI-engine", "max_stars_repo_head_hexsha": "cc2713fa01e5288bae21cfeb595729d665e092d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
"""Class to train a PyTorch model"""
import os
import json
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from ... | {"hexsha": "68db89e9d4252a18a5d77cca4241a9fdcbfc8dda", "size": 9255, "ext": "py", "lang": "Python", "max_stars_repo_path": "rssnet/learners/model.py", "max_stars_repo_name": "ArthurOuaknine/RSS-Net", "max_stars_repo_head_hexsha": "95e5f3df9f0fe4af7d37ff28f786141e48f14937", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import os
import pandas as pd
import torch
import torch.nn as nn
from PIL import Image
from torch.utils.data import Dataset
from torchvision import models, transforms
from tqdm import tqdm
import numpy as np
import pickle
data_dir = 'images'
metadata_fn = "metadata.json"
features_dir = "features"
features_file = os.p... | {"hexsha": "c8b87d68a0ad1180811e4607928f9318d3f103ab", "size": 4848, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_prep/featurize_and_match.py", "max_stars_repo_name": "microsoft/art", "max_stars_repo_head_hexsha": "3ea0616a449cc1c03ee49f1777ce6b331c520b57", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
% Written by:
% * Joachim Trescher
% * Hans de Vreught
\section{Types}
In \Booster\ a {\em type} is specified by an expression in
\Language{\NT{Type}} and represents a set of values. \Booster\ uses
structural type equivalence: two types are the same if their
definitions become the same when expanded; i.e.\ when all ... | {"hexsha": "ace54780e8b2fa6bf44fff22d3bd6a64c0e6ce3c", "size": 11536, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "compiler/frontends/toyfront/doc/Types.tex", "max_stars_repo_name": "CvR42/timber-compiler", "max_stars_repo_head_hexsha": "fcd35f78378dff111902052916f76adb037f2cf1", "max_stars_repo_licenses": ["Na... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
old_version = False
class GaussianConditional(nn.Module):
def __init__(self, dim, clip=False):
super(GaussianConditional, self).__init__()
self.... | {"hexsha": "581096bea9835dbde03cc6b3b1784ce161be3456", "size": 8910, "ext": "py", "lang": "Python", "max_stars_repo_path": "pixelcnn_model.py", "max_stars_repo_name": "zcwang0702/ssl_bad_gan", "max_stars_repo_head_hexsha": "a3d25b852ef5f17ea788b7141f0be0ac4d9fd631", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import pandas as pd
import os
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.externals.six import StringIO
import pydot
def get_csv():
if os.path.exists("Update2.csv"):
df = pd.read_csv("Update2.csv")
... | {"hexsha": "a9e778a85ae453647e6e08666caf272a49f5adca", "size": 1337, "ext": "py", "lang": "Python", "max_stars_repo_path": "MachineLearning/CSV_Prediction.py", "max_stars_repo_name": "SKravitsky/MachineLearningServer", "max_stars_repo_head_hexsha": "170081fe1ea53be7394e35ce208bd665ae002b73", "max_stars_repo_licenses": ... |
# author: Arun Ponnusamy
# website: https://www.arunponnusamy.com
# import necessary packages
from keras.preprocessing.image import img_to_array
from keras.models import load_model
from keras.utils import get_file
import numpy as np
import argparse
import cv2
import os
import cvlib as cv
# handle command line argumen... | {"hexsha": "a1f5f114841e45c0cfa17595c5f5e8f0aa2ef007", "size": 2389, "ext": "py", "lang": "Python", "max_stars_repo_path": "detect_gender.py", "max_stars_repo_name": "bekturaitbaev/GenderDetection", "max_stars_repo_head_hexsha": "c7a449879daf8eabc38486c7afbe02f7e667c33a", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\problemname{Arithmetic Decoding}
Arithmetic coding is a method to represent a message as a real number
$x$ such that $0 \leq x < 1$. We will assume that the
message consists only of uppercase `A's and `B's. The two letters
have associated probabilities $p_A$ and $p_B = 1 - p_A$ such that
$0 < p_A < 1$.
The current ... | {"hexsha": "91a1b6ed7053ccc1df631fc84988e51b8d8193f4", "size": 1846, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "problems/arithmeticdecoding/problem_statement/problem.tex", "max_stars_repo_name": "icpc/na-rocky-mountain-2020-public", "max_stars_repo_head_hexsha": "d77cd0dfd9bd707f34497977251c4cc583647fef", "ma... |
# Copyright 2016, Iain Dunning, Joey Huchette, Miles Lubin, and contributors
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
########################################... | {"hexsha": "2ee1b54da18460d4eae9d1a113b3e6d42f4636fa", "size": 1646, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "public/.julia/v0.5/JuMP/test/hockschittkowski/hs112.jl", "max_stars_repo_name": "Giarcr0b/MVO_Tool", "max_stars_repo_head_hexsha": "8f3348b8b56968febca8307acea3ebe1817fccae", "max_stars_repo_licens... |
\chapter*{Abbreviations}
\begin{acronym}[Kurzformsdf]
\acro{ECU}{Electrical Control Unit}
\acro{VM}{Virtual Machine}
\acro{SoC}{System on a Chip}
\acro{NIST}{National Institute for Standards and Technology}
\acro{IaaS}{Infrastructure-as-a-Service}
\acro{PaaS}{Platform-as-a-Service}
\acro{Sa... | {"hexsha": "2435946ee6d0ff6e1e51811277284d2c4e8196d5", "size": 1184, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/glossary.tex", "max_stars_repo_name": "MaxMalinowski/BachelorThesis", "max_stars_repo_head_hexsha": "91a5286a3d1cd32e628b85efcc2194a013cc7233", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 16 11:59:55 2020
@author: José Carlos Aradillas jaradillas@us.es
"""
import numpy as np
import matplotlib.pyplot as plt
from labcomdig import detectaSBF, simbolobit
def load_signal(filename):
try:
Xt = np.load(filename)
except File... | {"hexsha": "b321f8c96c5cff3e0fb6605122444da9fc1c8084", "size": 1638, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "josarajar/labcomdig-extra", "max_stars_repo_head_hexsha": "8c81be58d1edd6a4d88598a88bc4e83c22de6b24", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from numba import njit, generated_jit, vectorize, types
import numpy as np
from . import settings
from .constants import _MAXCOSH
@njit('float64(float64)', cache=settings.CACHE)
def _dsinpi(x):
"""Compute sin(pi*x) for real arguments."""
s = 1.0
if x < 0.0:
x = -x
s = -1.0
r = np.fmo... | {"hexsha": "de6a4b18526b52a76b4abc4d56bd2846f81496c6", "size": 4215, "ext": "py", "lang": "Python", "max_stars_repo_path": "spycial/trig.py", "max_stars_repo_name": "person142/spycial", "max_stars_repo_head_hexsha": "d017048c8c09ee0714f438cb75c2e221e068baee", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
/*
* 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)
*
* (C) Copyright 2007 Anthony Williams
* (C) Copyright 2011-2012 Vicente J. Botet Escriba
* (C) Copyright 2013 Andrey Semashev
*/
#ifndef BOOS... | {"hexsha": "4b8f8fb780204ce0352b0eda312a31f8dff1cf24", "size": 965, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/sync/locks/lock_options.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0"], ... |
import json
from PIL import Image
import numpy as np
import copy
import os
class armorstandgeo:
def __init__(self, name, alpha = 0.8,offsets=[9,0,0], size=[64, 64, 64], ref_pack="Vanilla_Resource_Pack"):
self.ref_resource_pack = ref_pack
## we load all of these items containing the mapping of bloc... | {"hexsha": "cddf6168b552bc1ad6ff7632ad322fdd3c01e7a4", "size": 19849, "ext": "py", "lang": "Python", "max_stars_repo_path": "armor_stand_geo_class.py", "max_stars_repo_name": "AL-Hareth/Structura", "max_stars_repo_head_hexsha": "56b5ea24960fb908f16ef97e82f402fcaf3690f1", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
```python
%matplotlib inline
```
<style type="text/css">
.reveal h1, .reveal h2 {
font-family:"League Gothic"
}
</style>
Hello everyone! My name is Amit Saha and today I am here to talk to about "Doing Math with Python".
Thank you for coming to my talk - i know you could have chosen the other talk, so it's good to... | {"hexsha": "7760f707bbfc05213b896561a6e4f7776f2c359d", "size": 52447, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "slides.ipynb", "max_stars_repo_name": "WhitakerLab/slides", "max_stars_repo_head_hexsha": "a1b36f4114ddb1d330795cd9b0967d476c493df2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma keys_empty [simp]: "keys DAList.empty = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. keys DAList.empty = {}
[PROOF STEP]
by transfer simp | {"llama_tokens": 64, "file": "Containers_AssocList", "length": 1} |
C *********************************************************
C * *
C * TEST NUMBER: 09.01.03.02/12 *
C * TEST TITLE : Error 113 handled by system *
C * *
C * PHIGS V... | {"hexsha": "7f327af6c022afd675f0810428e079189c23f1a1", "size": 578, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "third_party/Phigs/PVT/PVT_fort/09/01/03/02/p12.f", "max_stars_repo_name": "n1ckfg/Telidon", "max_stars_repo_head_hexsha": "f4e2c693ec7d67245974b73a602d5d40df6a6d69", "max_stars_repo_licenses": ["MI... |
import argparse
import copy
from io import UnsupportedOperation
import json
import os
import sys
import os.path as osp
from collections import OrderedDict
try:
import apex
except:
print("No APEX!")
import numpy as np
import torch
import yaml
from det3d import __version__, torchie
from det3d.datasets import bui... | {"hexsha": "9b56fbceb8765892f3b382598c59758685a359bf", "size": 6303, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/convert_voxelnet.py", "max_stars_repo_name": "alsun-oven/CenterPoint", "max_stars_repo_head_hexsha": "cafd89c4008270e648e97202bc256aff968e8109", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
import numpy as np
from scipy.special import erf
from ReplicatedFocusingBeliefPropagation import atanherf
__author__ = ["Nico Curti", "Daniele Dall'Olio"]
__email__ = ['nico.curti2@unibo.it', 'danie... | {"hexsha": "dc97091f331e610e6c4413d335eee579910cffe0", "size": 772, "ext": "py", "lang": "Python", "max_stars_repo_path": "ReplicatedFocusingBeliefPropagation/rfbp/test/test_atanherf.py", "max_stars_repo_name": "DanielLenz/rFBP", "max_stars_repo_head_hexsha": "fc8ae71a8ff58858f6800eeb3a3f25a56c143d18", "max_stars_repo_... |
import copy
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mplt
from matplotlib import pyplot as plt
from labellines import labelLines
from disease_spread_model.config import Directories
from disease_spread_model.names import TRANSLATE
from model... | {"hexsha": "6127382b480128ffd4659513bd567107c3274522", "size": 10583, "ext": "py", "lang": "Python", "max_stars_repo_path": "disease_spread_model/model/param_stability_tests.py", "max_stars_repo_name": "BartoszBiernacki/ABM", "max_stars_repo_head_hexsha": "6c993356aab86ce3d456e228c0a3aa41b2efa10c", "max_stars_repo_lice... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""Generate map plots.
Example::
$ python plot_maps.py -c : plot from files from combined
output file
$ python plot_maps.py -m max_id : plot from files with maximal subset
... | {"hexsha": "ad40e4e8ed5fde41be59a65aad0b09d5dd9e6efd", "size": 46574, "ext": "py", "lang": "Python", "max_stars_repo_path": "AWERA/resource_analysis/plot_maps.py", "max_stars_repo_name": "lthUniBonn/AWERA", "max_stars_repo_head_hexsha": "fa7f210516318bcfcbe1c99abbb5954b0cbaf682", "max_stars_repo_licenses": ["MIT"], "ma... |
from math import pow
import numpy as np
def DEFAULT_ESCORT(x):
"""Gives Shahshahani metric and KL-divergence."""
return x
def twisted_escort(x):
l = list(x)
return np.array([l[1], l[2], l[0]])
def power_escort(q):
"""Returns an escort function for the power q."""
def g(x):
y = []
... | {"hexsha": "1342fdaf8233925bf44deddcfaaf9845924526d6", "size": 906, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyed/geometries.py", "max_stars_repo_name": "jsafyan/pyed", "max_stars_repo_head_hexsha": "0d9c2b860a94f5ce0fc89ee48e1e76a86257750c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
% Process from LaTeX via XML to XHTML with
% latexml --destination installguide.xml --xml installguide.tex
% latexmlpost --destination installguide.xhtml --format=xhtml installguide.xml
%
% Crude hack to remove ugly symbols:
% sed -e 's/[§]//g' -i installguide.xhtml
%
% Strip off header for pasting into the website at... | {"hexsha": "602d3f2a673b48896969face372c6de184fca1bb", "size": 37402, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "gromacs-4.6.5/admin/installguide/installguide.tex", "max_stars_repo_name": "farajilab/gifs_release", "max_stars_repo_head_hexsha": "ffa674110bcd15de851a8b6a703b4f4bc96fcd2d", "max_stars_repo_licens... |
"""
Module defining Population class and methods
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import map
from builtins import range
try:
basestring
except NameError:
basestring = str
fro... | {"hexsha": "fa0f88640632bfc0f79539a80dfff764b32da6e2", "size": 25337, "ext": "py", "lang": "Python", "max_stars_repo_path": "netpyne/network/pop.py", "max_stars_repo_name": "FernandoSBorges/netpyne", "max_stars_repo_head_hexsha": "e1a7adb56b94aa78f8461397319eb4e9754c2d75", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# this follows `markdown.jl` but spurred by bugs/issues
function inter(st::String)
steps = explore_md_steps(st)
return steps[:inter_md].inter_md, steps[:inter_html].inter_html
end
@testset "issue163" begin
st = raw"""A _B `C` D_ E""" * J.EOS
imd, ih = inter(st)
@test imd == "A _B ##JDINSERT## D_... | {"hexsha": "72604427c2f096f5bf7dcbbefefba8af0c850bf1", "size": 3119, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/converter/markdown2.jl", "max_stars_repo_name": "davidanthoff/JuDoc.jl", "max_stars_repo_head_hexsha": "9435d89859d5e3b434af43aedb482053ba5eef09", "max_stars_repo_licenses": ["MIT"], "max_star... |
module a
implicit none
contains
subroutine b()
print *, "b()"
call c()
contains
subroutine c()
print *, 5
end subroutine c
end subroutine b
end module
program nested_02
use a, only: b
implicit none
call b()
end
| {"hexsha": "f3e60f0cd77dafe0966afd6d9aa82acfd9098762", "size": 229, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "integration_tests/nested_02.f90", "max_stars_repo_name": "Thirumalai-Shaktivel/lfortran", "max_stars_repo_head_hexsha": "bb39faf1094b028351d5aefe27d64ee69302300a", "max_stars_repo_licenses": ["BS... |
import numpy as np
import math
import grid_generate as GridGen
import estimate_method as EstMeth
import frequency_oracle as FreOra
import itertools
import choose_granularity
class AG_Uniform_Grid_1_2_way_optimal:
def __init__(self, args = None):
self.args = args
self.group_attribute_num = 2 # to c... | {"hexsha": "031a077c992e5e52d6be7681ca35d8686fce976f", "size": 15980, "ext": "py", "lang": "Python", "max_stars_repo_path": "HDG.py", "max_stars_repo_name": "YangJianyu-bupt/privmdr", "max_stars_repo_head_hexsha": "c4b68c87aeeb811ae8c42db511704fd3cc258a3c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import re
import os
import pandas as pd
import numpy as np
import datetime
# from sklearn.feature_extraction import DictVectorizer
# from sklearn.ensemble import GradientBoostingClassifier
# from sklearn.preprocessing import Imputer
import pyodbc
from config import Config
conf = Config()
INPUT_DIR = conf.input_folder... | {"hexsha": "278293950a2c2b5650a02cda4984924294f26e22", "size": 76674, "ext": "py", "lang": "Python", "max_stars_repo_path": "reference/auto_refresh_final-test-20170818T100421Z-001/auto_refresh_final-test/original_code.py", "max_stars_repo_name": "aniruddhasanyal/auto_wrk", "max_stars_repo_head_hexsha": "970156f7cb5205a... |
from multiprocessing import Process
import netCDF4 as nc
import numpy as np
import pandas as pd
import sys
import json
import time
import matplotlib.pyplot as plt
from urllib.request import urlopen
from math import sin, cos, sqrt, atan2, radians
# CLASS FOR ORGANIZING THE TOP CITIES
class city:
def __init__(self, ... | {"hexsha": "e12d043abb87bcd3ef2be1903ee9f817dc5aa1a6", "size": 4253, "ext": "py", "lang": "Python", "max_stars_repo_path": "appendix/adding_cities.py", "max_stars_repo_name": "siddkahal/MastersThesis_Latex_Doc", "max_stars_repo_head_hexsha": "6eff0b7138abd77ab46c7f6149a6bdf1054f7c40", "max_stars_repo_licenses": ["MIT"]... |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "8a1d6fdd27a9d8b75beb4bdb6065d672b7922654", "size": 54953, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_estimator/python/estimator/head/multi_label_head_test.py", "max_stars_repo_name": "ziky90/estimator", "max_stars_repo_head_hexsha": "825c02ce244ce21ec4f01360dfdf90cbf92f6bde", "max_sta... |
[STATEMENT]
lemma MSB_eq0_D: "MSB I = 0 \<Longrightarrow> x < length I \<Longrightarrow> I ! x = {||}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>MSB I = 0; x < length I\<rbrakk> \<Longrightarrow> I ! x = {||}
[PROOF STEP]
unfolding MSB_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>(if ... | {"llama_tokens": 214, "file": "Formula_Derivatives_WS1S_Prelim", "length": 2} |
# -*- coding: utf-8 -*-
r'''This module contains a number of classes for modeling neural nets in Theano.
Neural networks are really just a concise, computational way of describing a
mathematical model of some data. Before getting into the models below, we'll
first set up the ideas and notation that are used on this p... | {"hexsha": "725d5c280b18b1085fdb8e0d1270b24257f3ff2e", "size": 35996, "ext": "py", "lang": "Python", "max_stars_repo_path": "theanets/feedforward.py", "max_stars_repo_name": "hknerdgn/theanets", "max_stars_repo_head_hexsha": "64f9f65c3edd8a9bd74e8a414f2812b95557ff79", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
from scipy import optimize
class Portfolio:
def __init__(self, identifier, exp_ret, exp_vol, exp_cov, weights):
self.identifier = identifier
self.exp_ret = exp_ret
self.exp_vol = exp_vol
self.exp_cov = exp_cov
self.weights = weights
def portfolio_vo... | {"hexsha": "b4e6f8c75f5debb4d0ba606879e5d115b1b725b6", "size": 2580, "ext": "py", "lang": "Python", "max_stars_repo_path": "financier/portfolio.py", "max_stars_repo_name": "JakeVestal/financier", "max_stars_repo_head_hexsha": "0ace41f3b39bb2e4252cb90eeecf316767679e34", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""Evaluation utils for BLEU metrics."""
from collections import Counter
import math
import numpy as np
import subprocess
import sys
def bleu_stats(hypothesis, reference):
"""Compute statistics for BLEU."""
stats = []
stats.append(len(hypothesis))
stats.append(len(reference))
for n in range(1, 5)... | {"hexsha": "af482050c2a55271554137ecaaa9913c18c748da", "size": 1984, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/bleu.py", "max_stars_repo_name": "dingchaoz/suammarize_radiology", "max_stars_repo_head_hexsha": "7964b00596ced172fd75ad977435d00eef4a8a88", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
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