text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import socket # UDP通信用
import threading # マルチスレッド用
import time # ウェイト時間用
import numpy as np # 画像データの配列用
# import libh264decoder # H.264のデコード用(自分でビルドしたlibh264decoder.so)
class Tello:
"""Telloドローンと通信するラッパークラス"""
def __init__(self,... | {"hexsha": "60868eb91d50a098ba4708a40949387b7096cd65", "size": 12804, "ext": "py", "lang": "Python", "max_stars_repo_path": "tello.py", "max_stars_repo_name": "uzumal/Tello_Keyboard", "max_stars_repo_head_hexsha": "1065b03be6ac72f0afd3d235b171888e8e0427e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
"""Simple LSTM layer implementation.
Source: Andrej Karpathy (https://gist.github.com/karpathy/587454dc0146a6ae21fc)
"""
import numpy as np
class LSTM(object):
def init(self, n_input, n_hidden):
WLSTM = np.random.rand(n_input + n_hidden + 1, 4 * n_hidden) / np.sqrt(n_input + n_hidden)
WLSTM[0, :] = 0
return W... | {"hexsha": "404b6860f30a1f32cb1305767a60753741ec2800", "size": 3590, "ext": "py", "lang": "Python", "max_stars_repo_path": "lstm.py", "max_stars_repo_name": "prasanna08/MachineLearning", "max_stars_repo_head_hexsha": "5ccd17db85946630730ee382b7cc258d4fa866e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "... |
import os
import sys
import glob
import json
import scipy.signal as signal
import numpy.ma as ma
import numpy as np
import matplotlib
import matplotlib.pylab as plt
import matplotlib.dates as mdates
import datetime
import statsmodels.api as sm
lowess = sm.nonparametric.lowess
def savitzky_golay(y, window_size, order,... | {"hexsha": "b255acd0dcac5f174aa2904755980448a1a9bee3", "size": 7073, "ext": "py", "lang": "Python", "max_stars_repo_path": "time_series_scripts/tasks_generate_thumbs.py", "max_stars_repo_name": "openearth/eo-reservoir", "max_stars_repo_head_hexsha": "fab049d2a88fa59ebad682149b606e30c5e2f94c", "max_stars_repo_licenses":... |
from tkinter import *
import numpy as np
def bomb(A,x):
mine=0
if A[x]==1:
mine=-1
else:
if x+6<35:
if A[x+6]==1:
mine=mine+1
if x-6>0:
if A[x-6]==1:
mine=mine+1
if (x+1)%6!=0:
if A[x+1]==1:
mine=mine+1
if (x-1)%6!=5:
if A[x-1]==1:
mine=mine+1
return mine
... | {"hexsha": "855fd0a0416215779c4edd7f2084488b886ac585", "size": 17085, "ext": "py", "lang": "Python", "max_stars_repo_path": "minesweeper.py", "max_stars_repo_name": "preetam2030/Minesweeper", "max_stars_repo_head_hexsha": "e1be3cb985e7552a066fd09a492a2cd279d171ba", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
"""TODO(rpeloff)
Author: Ryan Eloff
Contact: ryan.peter.eloff@gmail.com
Date: October 2019
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from moonshot.baselines import fast_dtw
from moonshot.... | {"hexsha": "7e3272a041e13bbabf1ad1e11cc68fa904a6317e", "size": 10789, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/moonshot/baselines/dataset.py", "max_stars_repo_name": "rpeloff/moonshot", "max_stars_repo_head_hexsha": "f58ddaa15c2bea416731e3bd1f2c5de86d6aa115", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""Tools to Evaluate Recommendation models with Ranking Metrics."""
import numpy as np
def calc_ndcg_at_k(y_true: np.ndarray, y_score: np.ndarray, k: int) -> float:
"""Calculate a nDCG score for a given user."""
y_max_sorted = y_true[y_true.argsort()[::-1]]
y_true_sorted = y_true[y_score.argsort()[::-1]... | {"hexsha": "b68e1b974deb89e243861041fdf92710ed98a0d7", "size": 2191, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/evaluate/evaluator.py", "max_stars_repo_name": "usaito/asymmetric-tri-rec-real", "max_stars_repo_head_hexsha": "bd734362ffa498ba0ed44bdd536083246650949f", "max_stars_repo_licenses": ["Apache-2... |
#!/ebio/ag-neher/share/programs/bin/python2.7
#
#script that reads in precomputed repeated prediction of influenza and
#and plots the average prediction quality as a function of the diffusion constant and the
#scale parameter gamma.
#
#
import glob,argparse,sys
sys.path.append('/ebio/ag-neher/share/users/rneher/FluPred... | {"hexsha": "e839cd5cb31c3f4bae724482eba0f946f9ae27e0", "size": 5893, "ext": "py", "lang": "Python", "max_stars_repo_path": "flu/figure_scripts/fig4_s1_parameter_dependence.py", "max_stars_repo_name": "iosonofabio/FitnessInference", "max_stars_repo_head_hexsha": "3de97a9301733ac9e47ebc78f4e76f7530ccb538", "max_stars_rep... |
[STATEMENT]
lemma hd_sort_remdups: "hd (sort (remdups l)) = hd (sort l)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. hd (sort (remdups l)) = hd (sort l)
[PROOF STEP]
by (metis hd_sort_Min remdups_eq_nil_iff set_remdups) | {"llama_tokens": 107, "file": "Extended_Finite_State_Machines_FSet_Utils", "length": 1} |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 25 22:35:56 2021
@author: innat
"""
# ref: https://github.com/VcampSoldiers/Swin-Transformer-Tensorflow
# ref: https://keras.io/examples/vision/swin_transformers/
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.ker... | {"hexsha": "d7c7bf47f010ce3fee0a0fef88c0ee7c967c3613", "size": 12486, "ext": "py", "lang": "Python", "max_stars_repo_path": "swin_blocks.py", "max_stars_repo_name": "zoeyingz/EfficientNet-Hybrid-Swin-Transformer", "max_stars_repo_head_hexsha": "6adbe312e6f2406077fe8234c3c0a25547b4eeb6", "max_stars_repo_licenses": ["Apa... |
SUBROUTINE WGEOM(IA,IB,X,Y,Z,NM,NP,NAT,NSA,NPLA,VGA,BDSK,
2 ZLDA,NWG,VG,ZLD,WV,NFS1,NFS2)
COMMON /DIPOLES/ H1,H2,S
DIMENSION IA(1),IB(1),X(1),Y(1),Z(1),NSA(1),NPLA(1),BDSK(1)
COMPLEX VGA(1),ZLDA(1),VG(1),ZLD(1)
DATA H1,H2,S /1.,1.,.5/
C
C GEOMETRY FOR WIND
C
PRINT*,'WGEOM5, FOR MONOPOLE MAG BOOM AND ONE ANT... | {"hexsha": "a5c54be7a8a574786da8296b6fd3a06d1908a486", "size": 1764, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "WAVES_VMS_Fortran/PJK_Fortran/wind_dir/wgeom5_2.for", "max_stars_repo_name": "lynnbwilsoniii/Wind_Decom_Code", "max_stars_repo_head_hexsha": "ef596644fe0ed3df5ff3b462602e7550a04323e2", "max_star... |
import mdtraj as md
import time
import numpy as np
"""
A simple way to get conformations from a trajectory.
Provide phi and psi angle pairs to get PDBs of the molecule in these conformations.
These should correspond to energy wells on the free energy surface.
You are much better at peak picking than any algorithm I co... | {"hexsha": "462a707ae7046a286321633e9f382edc6f583902", "size": 2457, "ext": "py", "lang": "Python", "max_stars_repo_path": "extract_conformations.py", "max_stars_repo_name": "meyresearch/ANI-Peptides", "max_stars_repo_head_hexsha": "84684b484119699cb5458f4c2aed5fa8a482c315", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import numpy as np
from load_screens import load_screens
from scipy.special import stdtr
# Load batch-corrected screens
screens = load_screens()
# Remove cell lines with any missing genes
# (not required for DepMap 18Q3, but is for more recent releases)
# You can use other strategies to remove NaNs instead, like imp... | {"hexsha": "e88d049b942882fa1374fdd36567215a75d418d2", "size": 1736, "ext": "py", "lang": "Python", "max_stars_repo_path": "gene_pairs.py", "max_stars_repo_name": "kundajelab/coessentiality", "max_stars_repo_head_hexsha": "ae462f3073469245c84d85c7b49a4d5671f6b2a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
""" Prioritized Experience Replay implementations.
1. ProportionalSampler implements the proportional-based prioritization
using the SumTree in `data_structures.py`.
2. RankSampler implements the rank-based prioritization using the
PriorityQueue in `data_structures.py`.
"""
import torch
import numpy ... | {"hexsha": "4cc5f79525ea912a536923517a32347a5c98fd94", "size": 5670, "ext": "py", "lang": "Python", "max_stars_repo_path": "wintermute/replay/prioritized_replay.py", "max_stars_repo_name": "floringogianu/wintermute", "max_stars_repo_head_hexsha": "097aed1017192dff616bcd9c5083bb74c4aa71f6", "max_stars_repo_licenses": ["... |
from sklearn import linear_model
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import math
import os
from EnergyIntensityIndicators.pull_eia_api import GetEIAData
from EnergyIntensityIndicators.Residential.residential_floorspace import ResidentialFloorspace
from EnergyIntensi... | {"hexsha": "460e4970d0792e92b10c20113dcba627f279f00a", "size": 34359, "ext": "py", "lang": "Python", "max_stars_repo_path": "EnergyIntensityIndicators/weather_factors.py", "max_stars_repo_name": "NREL/EnergyIntensityIndicators", "max_stars_repo_head_hexsha": "6d5a6d528ecd27b930d82088055224473ba2d63e", "max_stars_repo_l... |
from networkx import *
z=[5,3,3,3,3,2,2,2,1,1,1]
print is_valid_degree_sequence(z)
print("Configuration model")
G=configuration_model(z) # configuration model
degree_sequence=list(degree(G).values()) # degree sequence
print("Degree sequence %s" % degree_sequence)
print("Degree histogram")
hist={}
for d in degree_seq... | {"hexsha": "feed33313bd4b5c0b922e55107819de224362aa6", "size": 462, "ext": "py", "lang": "Python", "max_stars_repo_path": "gra3.py", "max_stars_repo_name": "0x0mar/PyDetector", "max_stars_repo_head_hexsha": "e58e32c66c3972ceb0cbb65408e0ac797e896604", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 1, "max_... |
from keras.datasets import mnist
from ocr_cnn import OCR_NeuralNetwork
from keras.models import Sequential
from keras.layers import Merge
from preprocessing import preprocess_data
import numpy as np
class ensemble:
def __init__(self, models=[]):
self._models = []
for model in models:
self._models.append(model... | {"hexsha": "6026d5bfbdb19c4d9b3c4d1df4841f0803a1eb07", "size": 3923, "ext": "py", "lang": "Python", "max_stars_repo_path": "Notebooks/ensemble.py", "max_stars_repo_name": "ieee820/In-Codice-Ratio-OCR-with-CNN", "max_stars_repo_head_hexsha": "7b616822fe98d871ae1bf485ff7d0455922c84a2", "max_stars_repo_licenses": ["Apache... |
import pandas as pd
import numpy as np
import datetime
import json
import pickle
from pathlib import Path
from difflib import SequenceMatcher
from pickle_functions import *
from app_functions import *
from process_functions import write_log
path_input = Path.cwd() / 'input'
Path.mkdir(path_input, exist_ok = True)
path... | {"hexsha": "b177a7a686e568e7b1a45f5cc7371a2b4f0e6be8", "size": 15223, "ext": "py", "lang": "Python", "max_stars_repo_path": "df_process.py", "max_stars_repo_name": "Learning-from-the-curve/dashboard-belgium", "max_stars_repo_head_hexsha": "b8902a6347560ce622aef4e346e971b5b3a758ca", "max_stars_repo_licenses": ["MIT"], "... |
from matplotlib import pyplot as plt
from numpy import genfromtxt
vel_data = genfromtxt('vel_log.csv', delimiter=',')
accel_data = genfromtxt('accel_log.csv', delimiter=',') | {"hexsha": "49e39625b165d8e0397f2627b5489b3ba6a3d994", "size": 174, "ext": "py", "lang": "Python", "max_stars_repo_path": "igvc_ws/src/igvc_ekf/src/scripts/ekf_log_visual.py", "max_stars_repo_name": "SoonerRobotics/igvc_software_2022", "max_stars_repo_head_hexsha": "906e6a4fca22d2b0c06ef1b8a4a3a9df7f1d17dd", "max_stars... |
import os
import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
from src.dataset import CocoDataset, Resizer, Normalizer, Augmenter, collater
from src.model import EfficientDet
from tensorboardX import SummaryWriter
import shutil
import numpy as np... | {"hexsha": "1afcac5178aa399852d5ef4aadf82f1ebabb6e32", "size": 5852, "ext": "py", "lang": "Python", "max_stars_repo_path": "4_efficientdet/lib/infer_detector.py", "max_stars_repo_name": "deepchatterjeevns/Monk_Object_Detection", "max_stars_repo_head_hexsha": "861c6035e975ecdf3ea07273f7479dbf60fbf9b2", "max_stars_repo_l... |
[STATEMENT]
lemma axis_eq_0_iff [simp]:
shows "axis m x = 0 \<longleftrightarrow> x = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (axis m x = 0) = (x = (0::'a))
[PROOF STEP]
by (simp add: axis_def vec_eq_iff) | {"llama_tokens": 101, "file": null, "length": 1} |
#include "../include/sporkel.h"
#include <condition_variable>
#include <fstream>
#include <functional>
#include <map>
#include <mutex>
#include <numeric>
#include <string>
#include <thread>
#include <iostream>
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <boost/iostreams/filtering_stream.hpp>
#include <b... | {"hexsha": "3739bb8426f665ba2b99f0ee6d7272473b7d8f06", "size": 22756, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sporkel/src/patch.cpp", "max_stars_repo_name": "kc5nra/sporkel", "max_stars_repo_head_hexsha": "842ed61f4262b20098545b58aeee4db487143361", "max_stars_repo_licenses": ["0BSD"], "max_stars_count": 2.... |
# lets try to optimize this
import numpy as np
import scipy.misc as sc
import csv
import itertools
from itertools import combinations
import random
import pprint
import sys
import os
from evaluators import payout
from hand_scoring import get_hand_type
import timeit
start_time = timeit.default_timer()
print_color =... | {"hexsha": "de6379deeb3d036372319aad8478ab442111fc0e", "size": 3477, "ext": "py", "lang": "Python", "max_stars_repo_path": "video_poker_sim/optimized.py", "max_stars_repo_name": "nickweinberg/Python-Video-Poker-Sim", "max_stars_repo_head_hexsha": "f5a71da5c2c7e4926bd8b5f20fb83aa44dda56de", "max_stars_repo_licenses": ["... |
#include "test_qssintegrator.h"
#include <boost/format.hpp>
#include <iostream>
#include <string>
using std::cout;
using std::endl;
using boost::format;
void QSSTestProblem::odefun(double t, const dvector& y, dvector& q, dvector& d)
{
// csdfe(y, q, d, t)
// description:
// derivative function evaluator... | {"hexsha": "a58379ed058327c4d579fc762eaf1a245d2fba1c", "size": 8104, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/old/test_qssintegrator.cpp", "max_stars_repo_name": "BangShiuh/ember", "max_stars_repo_head_hexsha": "f0a70c7e01ae0dd7b5bd5ee70c8fc5d3f7207388", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
input := FileTools:-Text:-ReadFile("AoC-2021-17-input.txt" ):
| {"hexsha": "e4dbed639ecda1a5f2a33bb617d80b76e971fb6f", "size": 62, "ext": "mpl", "lang": "Maple", "max_stars_repo_path": "Day17/AoC17-Maple.mpl", "max_stars_repo_name": "johnpmay/AdventOfCode2021", "max_stars_repo_head_hexsha": "b51756bcebea662333072cf518cf040a962ef8b7", "max_stars_repo_licenses": ["CC0-1.0"], "max_sta... |
[STATEMENT]
lemma map_of_eq_None_iff:
"(map_of xys x = None) = (x \<notin> fst ` (set xys))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (map_of xys x = None) = (x \<notin> fst ` set xys)
[PROOF STEP]
by (induct xys) simp_all | {"llama_tokens": 109, "file": null, "length": 1} |
"""This module contains functions relating to function fitting"""
import matplotlib
import numpy as np
import datetime
from floodsystem.datafetcher import fetch, fetch_measure_levels
### TASK 2F
def polyfit(dates, levels, p):
"""Given the water level time history, this function computes the least squares polyn... | {"hexsha": "a384dacbb6a19142dbf67758a05671f45b4a6517", "size": 1372, "ext": "py", "lang": "Python", "max_stars_repo_path": "floodsystem/analysis.py", "max_stars_repo_name": "ryrolio/IA_Lent_Project", "max_stars_repo_head_hexsha": "9023dfb199b5db7676fef61f0fca46ab69707461", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#ifndef DERIVATIVES_H_5CHQ89V7
#define DERIVATIVES_H_5CHQ89V7
#include <gsl/gsl>
#include <tuple>
#include <type_traits>
namespace sens_loc::math {
/// Calculate the first derivate with the central differential quotient.
/// \tparam Real precision of the calculation
/// \param y__1 \f$y_{i-1}\f$
/// \param y_1 \f$y_... | {"hexsha": "1df168888a06557143f36074a538a3f354f6a9df", "size": 3458, "ext": "h", "lang": "C", "max_stars_repo_path": "src/include/sens_loc/math/derivatives.h", "max_stars_repo_name": "JonasToth/depth-conversions", "max_stars_repo_head_hexsha": "5c8338276565d846c07673e83f94f6841006872b", "max_stars_repo_licenses": ["BSD... |
import os
import cv2
import numpy as np
import tensorflow as tf
from datasets.constants import DatasetName, DatasetType
from datasets.constants import _N_TIME_STEPS
from datasets.msasl.constants import N_CLASSES as MSASL_N_CLASSES
from datasets.signum.constants import N_CLASSES as SIGNUM_N_CLASSES
from datasets.utils... | {"hexsha": "7194f81ea30ec05e04bcc1f2e1c02ec4855032b6", "size": 7383, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/tf_record_utils.py", "max_stars_repo_name": "rtoengi/transfer-learning-for-sign-language-recognition", "max_stars_repo_head_hexsha": "e0627115e6b68d6b85244d484011bb3895ccf4ee", "max_stars... |
import torch
import numpy as np
from typing import Optional, Tuple
from src.data.data_transform import DataTransform
import pywt
from pytorch_wavelets import DWTForward, DWTInverse
import pdb
class UNetWavTransform(DataTransform):
"""Pre-processor and post-processor to convert T4C data to
be compatible with Un... | {"hexsha": "586422c37c772638263cf14522cbb83d02349d8e", "size": 5051, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/transformwav.py", "max_stars_repo_name": "shehel/traffic_forecasting", "max_stars_repo_head_hexsha": "63c9fab665f7d48f621e8996290efd0d536dfc09", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
# !/usr/bin/python
"""
Created on Mar 18th 10:58:37 2016
train a continuous-time sequential model
@author: hongyuan
"""
import pickle
import time
import numpy
import theano
from theano import sandbox
import theano.tensor as tensor
import os
import sys
#import scipy.io
from collections import ... | {"hexsha": "7379ab15419c309db01afec181a8134fe0e09b35", "size": 14698, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_models.py", "max_stars_repo_name": "ZhaozhiQIAN/neurawkes", "max_stars_repo_head_hexsha": "1a3caa837b34f77ac9d078bc9bf10ff10a3bf959", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import numpy as np
import requests
import boto3
import semver
import json
from requests.auth import HTTPBasicAuth
from requests_toolbelt.multipart.encoder import MultipartEncoder
from requests_toolbelt.utils import dump
from zipfile import ZipFile
from model import train
from generate_datanpz import downloa... | {"hexsha": "8259a1b003dc4e57315a4ed5dff631dd86bd44a2", "size": 5693, "ext": "py", "lang": "Python", "max_stars_repo_path": "task-retrain/task.py", "max_stars_repo_name": "ingalls/ml-enabler", "max_stars_repo_head_hexsha": "efda973cb3fa9954cbe24cd0963a7b8f5be5ad6f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
import sys,os,re,time,cPickle
import numpy as np
from networkx import bidirectional_dijkstra,shortest_path_length
import networkx as nx
from scipy.cluster.vq import kmeans2
import scipy.stats as stats
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist,cdist,squareform
#from SpectralMix import SilV... | {"hexsha": "60c436453a683ba7f2a065145461bffda87be825", "size": 13442, "ext": "py", "lang": "Python", "max_stars_repo_path": "spectralmix/ClusterBase.py", "max_stars_repo_name": "ksiomelo/cubix", "max_stars_repo_head_hexsha": "cd9e6dda6696b302a7c0d383259a9d60b15b0d55", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# NB taken from the skimage docs
import numpy as np
import matplotlib.pyplot as plt
from skimage.data import shepp_logan_phantom
from skimage.transform import radon, rescale
image = shepp_logan_phantom()
image = rescale(image, scale=0.4, mode='reflect')
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))
ax1.set_... | {"hexsha": "dd13e4fe27614b2cd78078725bf3f3925ac79dd4", "size": 1510, "ext": "py", "lang": "Python", "max_stars_repo_path": "shearlet_admm/radon_demo.py", "max_stars_repo_name": "AndiBraimllari/SDLX", "max_stars_repo_head_hexsha": "f4a7280d261c00970fd8ab427174fba871f18a6d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
program t
200 parameter(a=1)
implicit integer(y)
parameter(b=2)
100 format (f4.2)
implicit real(kind=8)(i-k,r)
j=3.14
print 100,j
end program t
| {"hexsha": "21bd828cfe97b6b858b188bcf48f56b9c4b9fff0", "size": 190, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "tests/t0136x/t.f", "max_stars_repo_name": "maddenp/ppp", "max_stars_repo_head_hexsha": "81956c0fc66ff742531817ac9028c4df940cc13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "... |
@testset "DQNLearner" begin
env = CartPoleEnv(; T = Float32, seed = 11)
ns, na = length(rand(get_observation_space(env))), length(get_action_space(env))
agent = Agent(
policy = QBasedPolicy(
learner = DQNLearner(
approximator = NeuralNetworkApproximator(
... | {"hexsha": "481c2344b5c02bf3b673ad968942808cfd4d8927", "size": 2017, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/dqn.jl", "max_stars_repo_name": "findmyway/ReinforcementLearningZoo.jl", "max_stars_repo_head_hexsha": "8868ed5e2f2c4dfe725bec82f2bd4b0d08f365f9", "max_stars_repo_licenses": ["MIT"], "max_star... |
import settings
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import copy
import os, glob
import cv2
import random
import argparse
i... | {"hexsha": "a73309591cc35128dd3f3cf9b1fc5e2a8d65c03b", "size": 8619, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "chicm/scene", "max_stars_repo_head_hexsha": "1a18ed92b45ab21a9ca40f2f8030df7a4849b956", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_... |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Copyright (c) 2012 Barend Gehrels, Amsterdam, the Netherlands.
// Copyright (c) 2012 Bruno Lalande, Paris, France.
// Copyright (c) 2012 Mateusz Loskot, London, UK.
// This file was modified by Oracle on 2018, 2020.
// Modifications copyright (c) 2018, 2020, Or... | {"hexsha": "5b399d235a67c69d8e1a8b5b520df24ffbd31696", "size": 6462, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "mysql-server/include/boost_1_73_0/patches/boost/geometry/util/calculation_type.hpp", "max_stars_repo_name": "silenc3502/MYSQL-Arch-Doc-Summary", "max_stars_repo_head_hexsha": "fcc6bb65f72a385b9f56de... |
"""
Visualize the transformations
Matplotlib:
quiver plot
"""
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
# Function to plot a single transformation
def plot_transformation(transformation):
"""
Plot Transformation matrix
...
Parameters
---
tran... | {"hexsha": "b2238dea446d005761b3bf93a06b262e8a264024", "size": 4032, "ext": "py", "lang": "Python", "max_stars_repo_path": "Part-15-QuinticInterpolation/tools/visualize.py", "max_stars_repo_name": "SakshayMahna/Robotics-Mechanics", "max_stars_repo_head_hexsha": "3fa4b5860c4c9b4e22bd8799c0edc08237707aef", "max_stars_rep... |
#!/usr/bin/python
## ### ### ##
# ### ### #
# #
# ### ### ### #
# ### ### ### #
# # # # #
## # # # ##
import pandas as pd
from pylab i... | {"hexsha": "649e65338a799159c1d4ebb006e6793f25c74f91", "size": 2721, "ext": "py", "lang": "Python", "max_stars_repo_path": "fit_INFEST.py", "max_stars_repo_name": "A02l01/INFEST", "max_stars_repo_head_hexsha": "6cb201a745ea8c780d2b00f68124f2ae892d3ef4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
module AwesomeQuantumStates
using Yao
# GHZ
"""
GHZ state
"""
GHZ(n) = register(bit"0"^n) + register(bit"1"^n)
end # module
| {"hexsha": "25b868350b72ecf6a827860b39ede5fb58ef1a18", "size": 132, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AwesomeQuantumStates.jl", "max_stars_repo_name": "Roger-luo/AwesomeQuantumStates.jl", "max_stars_repo_head_hexsha": "5b98b70cc2d7da445995e61670f73e0ff605e58a", "max_stars_repo_licenses": ["Apach... |
from __future__ import print_function, division, absolute_import
import numpy as np
from keras.preprocessing.image import Iterator
from scipy import linalg
from scipy.signal import resample
import keras.backend as K
import warnings
from scipy.ndimage.interpolation import shift
class NumpyArrayIterator(Iterator):
... | {"hexsha": "922d24206d67762a4d2612c778fb18988cb5ffeb", "size": 25575, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/AudioDataGenerator.py", "max_stars_repo_name": "sushmit0109/ASSC", "max_stars_repo_head_hexsha": "8beda6f3d055a35fff9ae2ff417b38a38e2a7fa5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from time import sleep
import numpy as np
from keras.callbacks import Callback
class RussianRoulette(Callback):
"""Play a game of russian roulette.
# Arguments
rounds: int, number of bullets that will be loaded.
chambers: int, number of bullet chambers.
firings: int, number of time... | {"hexsha": "ebb6ace2ac0366d0f7009c3582195ff983a6c114", "size": 2936, "ext": "py", "lang": "Python", "max_stars_repo_path": "masochism/callbacks.py", "max_stars_repo_name": "simon-larsson/keras-masochism", "max_stars_repo_head_hexsha": "1d869d1a092b6c324f1183e292f95ccf235b3e4b", "max_stars_repo_licenses": ["MIT"], "max_... |
import cvxpy as cvx
import numpy as np
from scipy.optimize import root, minimize
from numpy.linalg import norm, inv, slogdet
import scipy.linalg as sla
from numpy import exp
import scipy.linalg as sla
import numpy.random as ra
import numpy.linalg as la
import scipy.stats
import ipdb
from Functions.objective_functions i... | {"hexsha": "9c83acd0a0813249c8ccb8160f28ceff9ecc00b1", "size": 42543, "ext": "py", "lang": "Python", "max_stars_repo_path": "blbandits3.py", "max_stars_repo_name": "kwangsungjun/lrbandit", "max_stars_repo_head_hexsha": "2f1f7ca4bbefe2bfd3e0bc50c4423a9791bfcde8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
""" Change the reference of an EEG signal
"""
import numpy
import warnings
from pySPACE.missions.nodes.base_node import BaseNode
from pySPACE.resources.data_types.time_series import TimeSeries
from pySPACE.resources.dataset_defs.stream import StreamDataset
class InvalidWindowException(Exception):
pass
class La... | {"hexsha": "5f7960417b1616d605a883827ac39d7a32e37304", "size": 12334, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySPACE/missions/nodes/spatial_filtering/rereferencing.py", "max_stars_repo_name": "pyspace/pyspace", "max_stars_repo_head_hexsha": "763e62c0e7fa7cfcb19ccee1a0333c4f7e68ae62", "max_stars_repo_lic... |
import evi
import pandas as pd
import numpy as np
import scipy
import harmonypy as hm
from sklearn.preprocessing import MinMaxScaler
def compute_lisi(adata, basis, batch_key, perplexity):
X = adata.obsm[basis]
metadata = pd.DataFrame(adata.obs[batch_key].values, columns = [batch_key])
lisi = hm.compute_lis... | {"hexsha": "6d7333631c2ee477380b3b9cb688d69fdb48f97b", "size": 2385, "ext": "py", "lang": "Python", "max_stars_repo_path": "evi/tools/evaluate.py", "max_stars_repo_name": "jranek/EVI", "max_stars_repo_head_hexsha": "7a4ec37dc847d02268241b464b296f00826c327d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# Copyright (c) 2019-2021, NVIDIA 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:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | {"hexsha": "587237e5188dfdebffe4f878986c44d1d3c1db62", "size": 6164, "ext": "py", "lang": "Python", "max_stars_repo_path": "qa/L0_backend_python/lifecycle/lifecycle_test.py", "max_stars_repo_name": "Mu-L/triton-inference-server", "max_stars_repo_head_hexsha": "ec1881d491cc2d2bd89ad9383724118e7121280c", "max_stars_repo_... |
from __future__ import print_function
try:
import h5py
from h5py import defs, utils, h5ac, _proxy # for py2app
except:
print ('Missing the h5py library (hdf5 support)...')
import gzip
import scipy.io
from scipy import sparse, stats, io
import numpy as np
import sys, string, os, csv, math
import time
sys.p... | {"hexsha": "f4244789d8fb3cf8a7178ed0e563cf1f011b73e5", "size": 17039, "ext": "py", "lang": "Python", "max_stars_repo_path": "stats_scripts/cell_collection.py", "max_stars_repo_name": "michalkouril/altanalyze", "max_stars_repo_head_hexsha": "e721c79c56f7b0022516ff5456ebaa14104c933b", "max_stars_repo_licenses": ["Apache-... |
import logging
from typing import Tuple
from numpy.random import uniform
from problems.test_case import TestCase, TestCaseTypeEnum
from problems.solutions.rock_star_climate import rock_temperature
logger = logging.getLogger(__name__)
FUNCTION_NAME = "rock_temperature"
INPUT_VARS = ['solar_constant', 'albedo', 'emis... | {"hexsha": "1ebb11702d95311dd1117b0e662087e0dbb25417", "size": 3059, "ext": "py", "lang": "Python", "max_stars_repo_path": "problems/rock_star_climate.py", "max_stars_repo_name": "benallan/lovelace-problems", "max_stars_repo_head_hexsha": "3780d2bfc58fe0531d60a92ae0a6c45e9814f58f", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
lemma less_multiset\<^sub>H\<^sub>O:
"M < N \<longleftrightarrow> M \<noteq> N \<and> (\<forall>y. count N y < count M y \<longrightarrow> (\<exists>x>y. count M x < count N x))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (M < N) = (M \<noteq> N \<and> (\<forall>y. count N y < count M y \<longright... | {"llama_tokens": 177, "file": null, "length": 1} |
# -*- coding: utf-8 -*-
# -*- mode: python -*-
""" Python reference implementations of model code
CODE ORIGINALLY FROM
https://github.com/melizalab/mat-neuron/blob/master/mat_neuron/_pymodel.py
"""
from __future__ import division, print_function, absolute_import
import numpy as np
#from mat_neuron.core import impuls... | {"hexsha": "9ef4e7abf472f34c8d6941b6451c46b92445cfd5", "size": 5377, "ext": "py", "lang": "Python", "max_stars_repo_path": "jithub/models/mat.py", "max_stars_repo_name": "russelljjarvis/numba_reduced_neuronal_models", "max_stars_repo_head_hexsha": "bc500aefab267a1a1eaf2a1d8dac83da676d7ee6", "max_stars_repo_licenses": [... |
{-# OPTIONS --cubical --no-import-sorts --safe #-}
open import Cubical.Core.Everything
open import Cubical.Relation.Binary.Raw
module Cubical.Relation.Binary.Reasoning.PartialOrder
{c ℓ} {A : Type c} (P : PartialOrder A ℓ) where
open PartialOrder P
import Cubical.Relation.Binary.Raw.Construct.NonStrictToStrict _≤_... | {"hexsha": "32ed96c8a648cd46fe2d8901e41ceb4a466dfa34", "size": 655, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/Relation/Binary/Reasoning/PartialOrder.agda", "max_stars_repo_name": "bijan2005/univalent-foundations", "max_stars_repo_head_hexsha": "737f922d925da0cd9a875cb0c97786179f1f4f61", "max_stars_... |
function [nu, g] = orderedNoiseUpdateParams(noise, mu, varsigma, y, index)
% ORDEREDNOISEUPDATEPARAMS Update parameters for ordered categorical noise model.
% NOISE
% NOISE
[g, dlnZ_dvs] = orderedNoiseGradVals(noise, mu(index, :), ...
varsigma(index, :), ...
... | {"author": "SheffieldML", "repo": "GPmat", "sha": "4b5914a38ecbad9fb7a13a3392970bfc28c9d911", "save_path": "github-repos/MATLAB/SheffieldML-GPmat", "path": "github-repos/MATLAB/SheffieldML-GPmat/GPmat-4b5914a38ecbad9fb7a13a3392970bfc28c9d911/noise/orderedNoiseUpdateParams.m"} |
from scipy.io import loadmat
import h5py
import pandas as pd
import seaborn as sns
diag_kws = {'bins': 50, 'color': 'teal', 'alpha': 0.4, 'edgecolor':None}
plot_kws = {'color': 'teal', 'edgecolor': None, 'alpha': 0.1}
path = "/media/robbis/DATA/meg/reftep/derivatives/phastimate/"
columns = ['phases32', 'hjort', 'p... | {"hexsha": "98e8177c3c678cc4d5e19d96398ec72468f06d8c", "size": 10490, "ext": "py", "lang": "Python", "max_stars_repo_path": "mvpa_itab/script/mambo/reftep/reftep_replica_results.py", "max_stars_repo_name": "robbisg/mvpa_itab_wu", "max_stars_repo_head_hexsha": "e3cdb198a21349672f601cd34381e0895fa6484c", "max_stars_repo_... |
# coding: utf-8
# Script to demo scikit for tweet popular/unpopular classification.
# In[1]:
from __future__ import division
from __future__ import print_function
import csv
import datetime as dt
import os
import platform
import sys
import numpy as np
import pandas
from sklearn import preprocessing
from sklearn im... | {"hexsha": "44b8f61c42d74976e78f5ee63affd7071b113a41", "size": 5547, "ext": "py", "lang": "Python", "max_stars_repo_path": "public_talks/2016_02_26_columbia/do_ml_on_feature_tables (all.csv).py", "max_stars_repo_name": "kylepjohnson/ipython_notebooks", "max_stars_repo_head_hexsha": "7f77ec06a70169cc479a6f912b4888789bf2... |
#!/usr/bin/env python3
# plotCsv - Create simple plots from a CSV file.
# Dave McEwan 2020-04-29
#
# Run like:
# plotCsv mydata.csv
# OR
# cat mydata.csv | plotCsv -o myplot
import argparse
import functools
import matplotlib
matplotlib.use("Agg") # Don't require X11.
import matplotlib.pyplot as plt
import nu... | {"hexsha": "9be02203822770b513434c26b0b0e33ba95da0a4", "size": 8032, "ext": "py", "lang": "Python", "max_stars_repo_path": "dmppl/scripts/plotCsv.py", "max_stars_repo_name": "DaveMcEwan/dmppl", "max_stars_repo_head_hexsha": "68e8a121d4591360080cd40121add1796ae48a1b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
using EzXML
using DataStructures
using LightGraphs
using Vulkan_Headers_jll:vk_xml
xdoc = readxml(vk_xml)
xroot = xdoc.root
include("utils.jl")
include("handles.jl")
include("graph.jl")
base_types_exceptions = Dict(
"CAMetalLayer" => "void",
"ANativeWindow" => "void",
"AHardwareBuffer" => "void",
)
vk_... | {"hexsha": "bd1e24b81c8e0e0720e990572c779790d7e89e9b", "size": 1808, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/spec/dev2.jl", "max_stars_repo_name": "serenity4/VulkanGen.jl", "max_stars_repo_head_hexsha": "cc876405ac0158d288062ed1b96fa586a633be89", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
#!/usr/bin/python3
__author__ = "Richa Bharti"
__copyright__ = "Copyright 2019-2022"
__license__ = "MIT"
__version__ = "0.1.0"
__maintainer__ = "Richa Bharti, Dominik Grimm"
__email__ = "richabharti74@gmail.com"
__status__ = "Dev"
import pandas as pd
import numpy as np
import argparse
import m... | {"hexsha": "690539516871c8798af906595436c01cd5386c98", "size": 8161, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/occurrence_based_ranking_analysis.py", "max_stars_repo_name": "grimmlab/transcriptional-translational-coupling", "max_stars_repo_head_hexsha": "3dec2d7c25973b4c37f1810468d4778726f96f0f", "max_... |
import numpy as np
np.sin.nin + "foo" # E: Unsupported operand types
np.sin(1, foo="bar") # E: Unexpected keyword argument
np.sin(1, extobj=["foo", "foo", "foo"]) # E: incompatible type
np.abs(None) # E: incompatible type
| {"hexsha": "ae7833de6c1a5d004bc18964317bf3e8941c198d", "size": 235, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/fail/ufuncs.py", "max_stars_repo_name": "JE-Chen/je_old_repo", "max_stars_repo_head_hexsha": "a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5",... |
/**********************************************************\
Original Author: Dan Weatherford
Imported with permission by: Richard Bateman (taxilian)
Imported: Aug 7, 2010
License: Dual license model; choose one of two:
New BSD License
http://www.opensource.org/licenses/bsd-license.php
... | {"hexsha": "e43b60a13b9b62b49f60a559a402853d8a10368f", "size": 1657, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "chrome/plugin/src/ScriptingCore/utf8_tools.cpp", "max_stars_repo_name": "Faham/bric-n-brac", "max_stars_repo_head_hexsha": "c886e0855869a794700eb385171bbf5bfd595aed", "max_stars_repo_licenses": ["Ap... |
{"mathlib_filename": "Mathlib.Tactic.GuardGoalNums", "llama_tokens": 0} | |
# !/usr/bin/env python3
# -*-coding:utf-8-*-
# @file: check_latex_label_ref.py
# @brief:
# @author: Changjiang Cai, ccai1@stevens.edu, caicj5351@gmail.com
# @version: 0.0.1
# @creation date: 23-01-2021
# @last modified: Mon 25 Jan 2021 06:07:03 PM EST
import numpy as np
#from PIL import Image
import glob
imp... | {"hexsha": "853c5f481ff671d995eef1701488912636ae6942", "size": 3338, "ext": "py", "lang": "Python", "max_stars_repo_path": "check_latex_label_ref.py", "max_stars_repo_name": "ccj5351/func_utility", "max_stars_repo_head_hexsha": "95a7ab515433cd012c6ae34bb4f970f4ad66e3f1", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""canonical_test.py"""
import numpy as np
import pytest
import scipy.linalg
from control.tests.conftest import slycotonly
from control import ss, tf, tf2ss
from control.canonical import canonical_form, reachable_form, \
observable_form, modal_form, similarity_transform, bdschur
from control.exception import Con... | {"hexsha": "0db6b924c0d8d2ee1795e290155fbf9f820dd0ce", "size": 17425, "ext": "py", "lang": "Python", "max_stars_repo_path": "control/tests/canonical_test.py", "max_stars_repo_name": "AI-App/Python-Control", "max_stars_repo_head_hexsha": "c2f6f8ab94bbc8b5ef1deb33c3d2df39e00d22bf", "max_stars_repo_licenses": ["BSD-3-Clau... |
[STATEMENT]
lemma interval_integral_eq_integral':
fixes f :: "real \<Rightarrow> 'a::euclidean_space"
shows "a \<le> b \<Longrightarrow> set_integrable lborel (einterval a b) f \<Longrightarrow> LBINT x=a..b. f x = integral (einterval a b) f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>a \<le> b; set... | {"llama_tokens": 208, "file": null, "length": 1} |
\section{Models}
\label{sec:models}
%A description of the models that you'll be using as baselines, and a preliminary description of the model or models that will be the focus of your investigation. At this early stage, some aspects of these models might not yet be worked out, so preliminary descriptions are fine.
In... | {"hexsha": "58b731be699a7d9fa410a354dce67dd95d389d81", "size": 1469, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "writeup_cs224u_expprotocol/models.tex", "max_stars_repo_name": "abgoswam/cs224u", "max_stars_repo_head_hexsha": "33e1a22d1c9586b473f43b388163a74264e9258a", "max_stars_repo_licenses": ["Apache-2.0"],... |
import numpy as np
import src.coding.codelength as codelength
import random
import collections
from infomap import Infomap
def merge_modules(trajectories, module_assignments, scheme="Huffman", init_module=True, init_node=True, deterministic=False, n_trials=10, n_itr=1000):
def smallest_module_pair(module_assignments_... | {"hexsha": "021188656a78573a0c336b8b2035abd577b281a9", "size": 3629, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/stMapEqn_Infomap.py", "max_stars_repo_name": "tatsuro-kawamoto/single-trajectory_map_equation", "max_stars_repo_head_hexsha": "5dbb4c564e9563a6f8f669319dce4842ce531736", "max_stars_repo_licens... |
import numpy as np
import nanotune as nt
from nanotune.tests.mock_classifier import MockClassifer
from nanotune.tuningstages.gatecharacterization1d import GateCharacterization1D
atol = 1e-05
def test_gatecharacterizaton1D_run(gatecharacterization1D_settings, experiment):
pinchoff = GateCharacterization1D(
... | {"hexsha": "ffcb77f4df4f112771b8bf09270d245e0464452b", "size": 622, "ext": "py", "lang": "Python", "max_stars_repo_path": "nanotune/tests/tuningstages/test_gatecharacterization1d.py", "max_stars_repo_name": "jenshnielsen/nanotune", "max_stars_repo_head_hexsha": "0f2a252d1986f9a5ff155fad626658f85aec3f3e", "max_stars_rep... |
// Copyright (c) 2014-2020 The Gridcoin developers
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include "main.h"
#include "gridcoin/support/block_finder.h"
#include <boost/test/unit_test.hpp>
#include <array>
#include <... | {"hexsha": "40feac136eb68a3375688c8933f8fe3938dec18a", "size": 2850, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/gridcoin/block_finder_tests.cpp", "max_stars_repo_name": "sweede-se/Gridcoin-Research", "max_stars_repo_head_hexsha": "48eb9482bd978b67cf9e8a795048438acab980c2", "max_stars_repo_licenses": ... |
.import cv2
import numpy as np
from skimage.measure import compare_ssim
def noiseCalibrate(cap,rob,bbLC,bbRC):
diffPercent=0
for i in range(30):
ret,frame=cap.read()
roi=frame[bbLC[0]:bbRC[0], bbLC[1]:bbRC[1]]
(score,diff)=compare_ssim(rob,roi,full=True,multichannel=True)
diffPe... | {"hexsha": "6a6e2bdb0f4fc5f6e81c3dcc0e91cacdde69f31f", "size": 3097, "ext": "py", "lang": "Python", "max_stars_repo_path": "Cam Code/oldCam.py", "max_stars_repo_name": "MackQian/Robotics1Project", "max_stars_repo_head_hexsha": "ca611bbd71dcca397a46941d4d40d720c8faa58c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
@doc raw"""a metric value for some object
IoK8sApiCustomMetricsV1beta1MetricValue(;
apiVersion=nothing,
kind=nothing,
describedObject=nothing,
... | {"hexsha": "63aaa23ac4bbb02d2dac85cc4f86046b51c2fa4d", "size": 5162, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ApiImpl/api/model_IoK8sApiCustomMetricsV1beta1MetricValue.jl", "max_stars_repo_name": "memetics19/Kuber.jl", "max_stars_repo_head_hexsha": "0834cab05d2b5733cb365594000be16f54345ddb", "max_stars... |
import torch
import torchvision
import torchvision.transforms as transforms
import json
# import matplotlib.pyplot as plt
import numpy as np
import time
import argparse
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from probprec import Preconditioner
torch.set_default_dtype(torch... | {"hexsha": "44ed6b4782d2bb4d9f7dd653370009da2c01b0e9", "size": 9295, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/cifar10_psgd.py", "max_stars_repo_name": "ludwigbald/probprec", "max_stars_repo_head_hexsha": "227a924a725551f4531cbe682da4830305f55277", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import sys
import os
import json
import time
import cantera as ct
import shutil
import copy
from PyQt4 import uic
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from src.core.def_tools import *
from src.ct.def_ct_tools import Xstr
from src.ct.senkin import senkin
from src.ct.psr import S_curve
from src.ck.de... | {"hexsha": "a3b81690fc4f19d6c3d20683c01eb8bbfdb27217", "size": 30165, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gui/def_run.py", "max_stars_repo_name": "haoxy97/GPS", "max_stars_repo_head_hexsha": "3da6d3a7410b7b7e5340373f206a1833759d5acf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
C TEST SUBROUTINE testos
C
INCLUDE 'VICMAIN_FOR'
SUBROUTINE MAIN44
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
C THIS IS A TEST FOR MODULE testos C
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
CALL TESTOS(IOS)
IF (IOS .EQ. 0) CALL XVMESSAGE('THE OS IS VMS',' ')
IF (IOS .EQ. 1) CALL XVME... | {"hexsha": "6b14e8edc8a147132fbe5098db2decb3cb7a3c95", "size": 433, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/sub/testos/test/ttestos.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
!
! This is the test program for EXTRAP
!
INCLUDE 'VICMAIN_FOR'
SUBROUTINE MAIN44
C-----THIS IS A TEST PROGRAM FOR MODULE EXTRAP
C-----EXTRAP WILL CALCULATE VALUES FOR THE DNS OF A LINE SEGMENT
C-----BASED ON THE VALUES OF OTHER POINTS IN THE PICTURE.
C-----THESE OTHER POINTS ARE STORED IN ARRAY PTS.
C... | {"hexsha": "bac76c94a2b40cb00d12f0e6434835bd78704b64", "size": 2267, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/sub/extrap/test/textrap.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
MODULE esn_I
INTERFACE
!...Generated by Pacific-Sierra Research 77to90 4.4G 10:47:13 03/09/06
SUBROUTINE esn ( AL, A, ESPI, OVL, CESPM2, CESPML, CESP, POTPT, ES&
, ESPC, WORK1D, NORBS, NUMAT)
USE vast_kind_param,ONLY: DOUBLE
integer, INTENT(IN) :: NORBS
inte... | {"hexsha": "692d41dbf67bb298ba1c78a41e0ff8303dedf9a8", "size": 799, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "2006_MOPAC7.1/src_interfaces/esn_I.f90", "max_stars_repo_name": "openmopac/MOPAC-archive", "max_stars_repo_head_hexsha": "01510e44246de34a991529297a10bcf831336038", "max_stars_repo_licenses": ["B... |
-- Enumerated Types
inductive weekday : Type
| sunday : weekday
| monday : weekday
| tuesday : weekday
| wednesday : weekday
| thursday : weekday
| friday : weekday
| saturday : weekday
#check weekday
#print weekday
#check weekday.sunday
#check weekday.monday
open weekday
#check sunday
#check monday
#check weekda... | {"author": "agryman", "repo": "theorem-proving-in-lean", "sha": "cf5a3a19d0d9d9c0a4f178f79e9b0fa67c5cddb9", "save_path": "github-repos/lean/agryman-theorem-proving-in-lean", "path": "github-repos/lean/agryman-theorem-proving-in-lean/theorem-proving-in-lean-cf5a3a19d0d9d9c0a4f178f79e9b0fa67c5cddb9/src/07-Inductive-Types... |
c !! This is used to get the error
double precision function qexact(blockno,xc,yc,t)
implicit none
integer blockno
double precision xc,yc,t
double precision x0, y0, u0, v0
double precision q0,qc
double precision u0_comm,v0_comm,revs_comm
common /comm_velocity/ u0_co... | {"hexsha": "72cee8dca071ca8c13f780646187e2714c96f8ba", "size": 1241, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "applications/clawpack/advection/2d/torus/qexact.f", "max_stars_repo_name": "MelodyShih/forestclaw", "max_stars_repo_head_hexsha": "2abaab636e6e93f5507a6f231490144a3f805b59", "max_stars_repo_licens... |
from typing import Any, Dict, List, Optional
import ConfigSpace as CS
import numpy as np
from tpe.optimizer.base_optimizer import BaseOptimizer, ObjectiveFunc
class RandomSearch(BaseOptimizer):
def __init__(
self,
obj_func: ObjectiveFunc,
config_space: CS.ConfigurationSpace,
res... | {"hexsha": "bedd81337be87225c18fd37ba2d2219523caa731", "size": 1866, "ext": "py", "lang": "Python", "max_stars_repo_path": "tpe/optimizer/random_search.py", "max_stars_repo_name": "nabenabe0928/AIST_TPE", "max_stars_repo_head_hexsha": "0094043aed9e148ea817bcdbd5c61c7659f779e0", "max_stars_repo_licenses": ["Apache-2.0"]... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 27 09:58:57 2020
@author: gao
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.axes_grid1 import AxesGrid
import matplotlib as mpl
import os
from matplotlib.colors import LinearSegmentedColormap... | {"hexsha": "0427d648c870d7a9d365daf12a45f9e8f97edf96", "size": 7326, "ext": "py", "lang": "Python", "max_stars_repo_path": "figure/Figure2C_general_size_effect.py", "max_stars_repo_name": "YuanxiaoGao/Evolution_of_reproductive_strategies_in_incipient_multicellularity", "max_stars_repo_head_hexsha": "13eb51639fcee630a76... |
import pyvisa
import feeltech
import time
import numpy
import matplotlib.pyplot as plt
import math
fichero = open('config.txt')
########################
timeDelay = 0.7 #Adjust the time delay between frequency increments (in seconds)
########################
startFreq = float(fichero.readline().split(',')[1]) #Read ... | {"hexsha": "8efa3dde410aff4aef7f0496146f4120ec706211", "size": 3817, "ext": "py", "lang": "Python", "max_stars_repo_path": "BodePlot.py", "max_stars_repo_name": "ailr16/BodePlot-DS1054Z", "max_stars_repo_head_hexsha": "1bb77b92b0c7249499e3d0748fff551210f5b81c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
#Author : Dhaval Harish Sharma
#Red ID : 824654344
#Assignment 3, Question A and B, Using user defined edge detection
"""Finding the edges in an image using user defined edge detection and changing the colors
of edges of different objects. After that, adding salt and pepper noise to the image,
again applying edge det... | {"hexsha": "47519676574cf6ac5b0b1998bc854e9fc0373fee", "size": 6250, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignment 3/QuestionAB_Sobel.py", "max_stars_repo_name": "dhavalsharma97/Computer-Vision", "max_stars_repo_head_hexsha": "75b39c9e5adb2a1a54854ed487fe750ad316a8fc", "max_stars_repo_licenses": ["M... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize']=12,9 # make the chart wider
import pycountry
df=pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-05/transit_cost.csv')
df.head()
df.info()
df.dropna(inplace=True) #... | {"hexsha": "2e9d48ccedb8f178c4e72deaeb6cac316f6594db", "size": 6117, "ext": "py", "lang": "Python", "max_stars_repo_path": "TidyTuesday/20210105-transit-cost.py", "max_stars_repo_name": "vivekparasharr/Challenges-and-Competitions", "max_stars_repo_head_hexsha": "c99d67838a0bb14762d5f4be4993dbcce6fe0c5a", "max_stars_rep... |
#! /usr/bin/env python3
import rospy
from geometry_msgs.msg import Point
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from tf import transformations
from std_srvs.srv import *
import math
import numpy as np
import matplotlib.pyplot as plt
room_center_found_ = True
active_ = False
current_... | {"hexsha": "d057e37e22c0a686d2a8145693dac71817586889", "size": 4681, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros_pkg/robot_function/scripts/find_room_center.py", "max_stars_repo_name": "Pallav1299/coppeliasim_ros", "max_stars_repo_head_hexsha": "3c4db53be7ea7d64c53c1d56066bb93dd212a476", "max_stars_repo_... |
[STATEMENT]
lemma var_assign_eval [intro!]: "(X x, s(x:=n)) -|-> (n, s(x:=n))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (X x, s(x := n)) -|-> (n, s(x := n))
[PROOF STEP]
by (rule fun_upd_same [THEN subst]) fast | {"llama_tokens": 108, "file": null, "length": 1} |
# Modified from https://github.com/MIC-DKFZ/nnunet
import pickle
import torch
import tensorboardX
import numpy as np
from collections import OrderedDict
import SimpleITK as sitk
def pickle_load(in_file):
with open(in_file, "rb") as opened_file:
return pickle.load(opened_file)
class AverageMeter(object):
... | {"hexsha": "9d1f0ca447a3da5ce6653365b13029d9c7eb054d", "size": 6262, "ext": "py", "lang": "Python", "max_stars_repo_path": "postprocess/utils.py", "max_stars_repo_name": "BruceResearch/BiTr-Unet", "max_stars_repo_head_hexsha": "d1f5ad5df7ff5e65c7797bfafd51a782f6114af3", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#!/usr/bin/env python
import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt
import copy
import random
import sift
class Calibrate():
def main():
# get image from webcam for now just read it in
img = cv2.imread("../images/saved.jpg", 1)
# Select crop region
crop_region = User_ROI_Se... | {"hexsha": "44b885f1b95edea64068ada3c7ba02614e68ea05", "size": 431, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pong_vision/src/calibrate.py", "max_stars_repo_name": "kekraft/golden_eye", "max_stars_repo_head_hexsha": "a857d9c31645451b09f68a148e996dfa213322ec", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
import cv2
import numpy as np
class DrawingClass(object):
def __init__(self):
self.draw_command ='None'
self.frame_count = 0
def drawing(self, frame, fps, num_egg, htc_egg, state):
cv2.putText(frame, 'FPS: {:.2f}'.format(fps),
(10, 30), cv2.FONT_HERSHEY_SIMP... | {"hexsha": "1cd41a80f04199f3be841ce38a8ac4428c343606", "size": 6620, "ext": "py", "lang": "Python", "max_stars_repo_path": "show/drawing.py", "max_stars_repo_name": "nohamanona/poke-auto-fuka", "max_stars_repo_head_hexsha": "9d355694efa0168738795afb403fc89264dcaeae", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct DedicatedHostAvailableCapacity <: SwaggerModel
allocatableVMs::Any # spec type: Union{ Nothing, Vector{DedicatedHostAllocatableVM} } # spec name: allocatableVM... | {"hexsha": "59490f850bf8b8c0988efb1cd6fa2103bb5f291e", "size": 1505, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Compute/ComputeManagementClient/model_DedicatedHostAvailableCapacity.jl", "max_stars_repo_name": "JuliaComputing/Azure.jl", "max_stars_repo_head_hexsha": "0e2b55e7602352d86bdf3579e547a74a9b5f44... |
import scrapy
import numpy
import pandas as pd
import csv
from arania_noticias.items import ComercioNew
from scrapy.loader import ItemLoader
from scrapy.loader.processors import TakeFirst
class SpiderNews(scrapy.Spider):
name = 'news'
urls = []
with open('urls.csv', 'r', encoding='utf-8') as urls_csv:
... | {"hexsha": "5508d6a5b28d0544913a0f2bae7ca4dfc01d08a2", "size": 2204, "ext": "py", "lang": "Python", "max_stars_repo_path": "proyecto-2b/ScrapyDataset/scrapy/arania_noticias/arania_noticias/spiders/spider_news.py", "max_stars_repo_name": "2020-A-JS-GR1/py-velasquez-revelo-jefferson-david", "max_stars_repo_head_hexsha": ... |
#!/usr/bin/python3
import flask
from flask import Flask, jsonify, request
from waitress import serve
import datetime
import os
import json
import io
import cld_steiner as process_cld
from PIL import Image
from pathutils import remove_consecutive_duplicates, resample_path, smooth_path
import numpy as np
import sys
def... | {"hexsha": "4a74035cce8a8c3ab658141ba60372210bfce4f7", "size": 1741, "ext": "py", "lang": "Python", "max_stars_repo_path": "gce/server.py", "max_stars_repo_name": "kylemcdonald/bsp", "max_stars_repo_head_hexsha": "e33c71f5924bef61a15e2b87230ac27b8f8261aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
///////////////////////////////////////////////////////////////////////////////
//
// http://protoc.sourceforge.net/
//
// Copyright (C) 2013 Bjorn Reese <breese@users.sourceforge.net>
//
// Permission to use, copy, modify, and distribute this software for any
// purpose with or without fee is hereby granted, provided ... | {"hexsha": "07b435e24882607e8f7fe6ab6090e38231cefb3f", "size": 26099, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/transenc/oarchive_suite.cpp", "max_stars_repo_name": "skyformat99/protoc", "max_stars_repo_head_hexsha": "f0a72275c92bedc8492524cb98cc24c5821c4f11", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
(* Author: Dmitriy Traytel *)
header {* Normalization of WS1S Formulas *}
(*<*)
theory WS1S_Normalization
imports WS1S
begin
(*>*)
fun nNot where
"nNot (FNot \<phi>) = \<phi>"
| "nNot (FAnd \<phi>1 \<phi>2) = FOr (nNot \<phi>1) (nNot \<phi>2)"
| "nNot (FOr \<phi>1 \<phi>2) = FAnd (nNot \<phi>1) (nNot \<phi>2)"
| "... | {"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/MSO_Regex_Equivalence/WS1S_Normalization.thy"} |
import sys
import json
import time
import torch
import pickle
import socket
import logging
import numbers
import functools
import subprocess
import unicodedata
from typing import List, Union
from pathlib import Path
import yaml
import numpy as np
import hickle
import scipy.io as spio
import msgpack_numpy as msgpack_np... | {"hexsha": "bd35eaf696d09a59f434fc7415fea2223583a2d5", "size": 18359, "ext": "py", "lang": "Python", "max_stars_repo_path": "zsvision/zs_utils.py", "max_stars_repo_name": "bjuncek/zsvision", "max_stars_repo_head_hexsha": "a84ecf93f334ecbdd99a8be7150fc767e732e6af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
from sklearn.metrics import confusion_matrix
from ninolearn.learn.skillMeasures import seasonal_correlation
seismic = plt.cm.get_cmap('seismic', 256)
newcolors = seismic(np.linspace(0, 1, 256))
grey = np.array([192/256, 19... | {"hexsha": "aa098c068b9c1cdb7be91afecaedef6abc399600", "size": 2924, "ext": "py", "lang": "Python", "max_stars_repo_path": "ninolearn/plot/evaluation.py", "max_stars_repo_name": "pjpetersik/ninolearn", "max_stars_repo_head_hexsha": "2a6912bbaaf3c5737f6dcda89e4d7d1fd885a35e", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Polygon
from geometry_tools.projective import ProjectivePlane
plane = ProjectivePlane()
plane.set_hyperplane_coordinates(np.array([1.0, 1.0, 1.0]))
plane.set_affine_origin([1.0, 1.0, 1.0])
plane.set_affine_direction([1.0, 0.0, 0.0], [0... | {"hexsha": "e21b7ee54a59334d85ace1a810cb104fc480f05b", "size": 1068, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/examples.py", "max_stars_repo_name": "tjweisman/geometry_tools", "max_stars_repo_head_hexsha": "9523dd86f68606d5297b228e874020d62663d3db", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma bin_rsplit_len_le: "n \<noteq> 0 \<longrightarrow> ws = bin_rsplit n (nw, w) \<longrightarrow> length ws \<le> m \<longleftrightarrow> nw \<le> m * n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n \<noteq> 0 \<longrightarrow> ws = bin_rsplit n (nw, w) \<longrightarrow> (length ws \<le> m) = (nw ... | {"llama_tokens": 162, "file": "Word_Lib_Bits_Int", "length": 1} |
import torch.nn as nn
import numpy as np
import torch
from functools import reduce
class MSSSIM(nn.Module):
def __init__(self, width, batch_size, n_channel,
cuda, c1=.01**2, c2=.02**2, n_sigmas=5):
super(MSSSIM, self).__init__()
self.c1 = c1
self.c2 = c2
sigm... | {"hexsha": "31f60078abc639ac4a9f90241a7a0d131521ba8f", "size": 2042, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/loss/msssim.py", "max_stars_repo_name": "muneebaadil/sisr-irl", "max_stars_repo_head_hexsha": "29ccf9ad970ade22fc8e158b83f952504db71a7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
// Copyright 2013-2015 Stanford University
//
// 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 ... | {"hexsha": "0866636090c1323793aefb50b3fbad5f11c7650d", "size": 7359, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tools/apps/specgen_statistics.cc", "max_stars_repo_name": "sdasgup3/strata-stoke", "max_stars_repo_head_hexsha": "b9981a48a82a72069896d29863649cfad1b4d98c", "max_stars_repo_licenses": ["ECL-2.0", "Ap... |
"""
Helper Classes and Functions for docking fingerprint computation.
"""
from __future__ import division
from __future__ import unicode_literals
__author__ = "Bharath Ramsundar and Jacob Durrant"
__license__ = "GNU General Public License"
import logging
import math
import os
import subprocess
import numpy as np
impo... | {"hexsha": "656aeae02526ae400d8b9f12fa0a8c0a5f15147c", "size": 17258, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepchem/feat/nnscore_utils.py", "max_stars_repo_name": "n3011/deepchem", "max_stars_repo_head_hexsha": "c316d998c462ce01032f0dae883856b400ea4765", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# In this example we do a few things to detect the edge
# 1. We define two filers (aka sobel filters) called Hx, Hy
# 2. We perform a convolution on Hx and Hy to get Gx, Gy
# 3. From there we calculate the edge detection output and solve for G
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
f... | {"hexsha": "710ca87a479c8025b0291ed50568ec979bb5dfb8", "size": 959, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/edge-detection.py", "max_stars_repo_name": "0w8States/numpy-stack-samples", "max_stars_repo_head_hexsha": "2fca4ee45cb532cc12d5646276ee53a7e4f0d0f2", "max_stars_repo_licenses": ["MIT"], "max_... |
import os
import torch
import numpy as np
import matplotlib.pyplot as plt
from torchid.statespace.module.ssmodels_ct import NeuralStateSpaceModel
from torchid.statespace.module.ss_simulator_ct import ForwardEulerSimulator
import gpytorch
import finite_ntk
import loader
from torchid import metrics
class StateSpaceWrap... | {"hexsha": "cc9faf53f9cdae76f19a17386125ec6a3c986f1b", "size": 4080, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/RLC/RLC_SS_transfer_gp.py", "max_stars_repo_name": "forgi86/RNN-adaptation", "max_stars_repo_head_hexsha": "d32e8185c6a746060dd726a0f5080231e0c9439b", "max_stars_repo_licenses": ["MIT"], ... |
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