text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
module LSystem
export @lsys
export LModel, add_rule!
export LState, next, result
using MacroTools
# L-System model definition
"""
A L-system model is represented by an axiom called `axiom`
and a set of rewriting `rules`.
"""
struct LModel
axiom
rules
end
"Create a L-system model."
LModel(axiom) = LModel([a... | {"hexsha": "1165c3a8c99f732a4a29364ff26b61192a3be6cd", "size": 2552, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LSystem.jl", "max_stars_repo_name": "tk3369/LSystem.jl", "max_stars_repo_head_hexsha": "9953ce229c6aa2cecbad3e0fd5ffa3c8a5e7da0e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
[STATEMENT]
lemma aux:
"
distinct (map fst (ts1@ts2)) \<Longrightarrow>
the_default (0::val) (case map_of ts1 (k, i) of None \<Rightarrow> map_of ts2 (k, i) | Some x \<Rightarrow> Some x)
= the_default 0 (map_of ts1 (k, i)) + the_default 0 (map_of ts2 (k, i))
"
[PROOF STATE]
proof (prove)
goal... | {"llama_tokens": 402, "file": "VerifyThis2019_Challenge3", "length": 2} |
import os
import numpy as np
import json
from ._base_dataset import _BaseDataset
from ..utils import TrackEvalException
from .. import utils
from .. import _timing
class YouTubeVIS(_BaseDataset):
"""Dataset class for YouTubeVIS tracking"""
@staticmethod
def get_default_dataset_config():
"""Defaul... | {"hexsha": "6d5b54c9ed9045c5f4e2f25e4234f87a00f8e302", "size": 19626, "ext": "py", "lang": "Python", "max_stars_repo_path": "trackeval/datasets/youtube_vis.py", "max_stars_repo_name": "AlexanderSing/TrackEval", "max_stars_repo_head_hexsha": "373e643f8989445f0253af6748e9e247d6ae6322", "max_stars_repo_licenses": ["MIT"],... |
from ..mapping import MappedArray, AccessType
from ..indexing import is_fullslice, split_operation, slicer_sub2ind, invert_slice
from .. import volutils
from ..readers import reader_classes
from .metadata import ome_zooms, parse_unit
from nitorch.spatial import affine_default
from nitorch.core import pyutils, dtypes
fr... | {"hexsha": "bc78abf7f8e2a8c907ac5c5f3f0c8027acf794de", "size": 16271, "ext": "py", "lang": "Python", "max_stars_repo_path": "nitorch/io/tiff/array.py", "max_stars_repo_name": "wyli/nitorch", "max_stars_repo_head_hexsha": "3ecd18944cf45fb9193c4c6ffc32953c4d1c71ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from __future__ import annotations
import warnings
from scvi.dataset.dataset import (
GeneExpressionDataset,
logger,
remap_categories,
CellMeasurement,
)
import numpy as np
import pandas as pd
import scipy.sparse as sp_sparse
import os
import torch
from collections import defaultdict
from concurrent.... | {"hexsha": "623b10dad9c55b19953cc3871452e573b31ab068", "size": 87839, "ext": "py", "lang": "Python", "max_stars_repo_path": "scvi/dataset/union.py", "max_stars_repo_name": "maichmueller/scVI", "max_stars_repo_head_hexsha": "95fe13a36f83e2b6390fc47e96a2d17ed1fa318d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# Based on PWSCF documentation (version 6.2)
function gen_lattice_cubic( a::Float64 )
v1 = a*[1,0,0]
v2 = a*[0,1,0]
v3 = a*[0,0,1]
#
LL = zeros(3,3)
LL[:,1] = v1
LL[:,2] = v2
LL[:,3] = v3
return LL
end
gen_lattice_sc(a::Float64) = gen_lattice_cubic(a)
function gen_lattice_fcc( a::... | {"hexsha": "99bda8549fc5726626fcf2718d1a163b6b54ddc4", "size": 6096, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "PW/common/gen_lattice_pwscf.jl", "max_stars_repo_name": "f-fathurrahman/ffr-ElectronicStructure.jl", "max_stars_repo_head_hexsha": "35dca9831bfc6a3e49bb0f3a5872558ffce4b211", "max_stars_repo_licens... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
from statistics import mean, variance
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
def plotCD(fig, data, reg1, reg2, log):
... | {"hexsha": "26d704cbf147d023f33e37747bda6b17df8e640b", "size": 10157, "ext": "py", "lang": "Python", "max_stars_repo_path": "CobbDouglas.py", "max_stars_repo_name": "barklan/cobb-dude", "max_stars_repo_head_hexsha": "5d1d2d3ee9032558b33b84ba857d7cc2279c4a1a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import numpy as np
from lib.Activations import Activation_Softmax
class Loss:
def remember_trainable_layers(self, trainable_layers):
self.trainable_layers = trainable_layers
def calculate(self, output, y, *, include_regularization=False):
sample_losses = self.forward(output, y)
data_l... | {"hexsha": "0ea298350ef6c8cfdf86193784229f99f10da684", "size": 4804, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/Losses.py", "max_stars_repo_name": "QuicksandDesignStudio/neural-network", "max_stars_repo_head_hexsha": "2bc6c533e1fb7dc11d43762d5fc62caaa670b55b", "max_stars_repo_licenses": ["MIT"], "max_st... |
import random
import os
import glob
import numpy as np
np.random.seed(0)
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchsat.transforms import transforms_cls
from skimage import io
from skimage.transform import rescale
class RandomApply(object):
... | {"hexsha": "f0b632aed5906ff90074f175742094c7a9afde02", "size": 5004, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_aug/dataset_wrapper.py", "max_stars_repo_name": "ezekielbarnett/SimCLR", "max_stars_repo_head_hexsha": "89f9e36f18b0da264a4faf833981198797b4a94b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
// Ogonek
//
// Written in 2012-2013 by Martinho Fernandes <martinho.fernandes@gmail.com>
//
// To the extent possible under law, the author(s) have dedicated all copyright and related
// and neighboring rights to this software to the public domain worldwide. This software is
// distributed without any warranty.
//
// ... | {"hexsha": "4d71b6f285911bac410ea4daee4ff81c2be2075d", "size": 4535, "ext": "h++", "lang": "C++", "max_stars_repo_path": "include/ogonek/encoding/iterator.h++", "max_stars_repo_name": "libogonek/ogonek", "max_stars_repo_head_hexsha": "46b7edbf6b7ff89892f5ba25494749b442e771b3", "max_stars_repo_licenses": ["CC0-1.0"], "m... |
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torchvision import datasets, transforms
import os
import argparse
import pdb
import copy
import numpy as np
from torch.optim import lr_sc... | {"hexsha": "dc006a71f6c96c0d4be65444d9e9aa8c5b3f14f8", "size": 16423, "ext": "py", "lang": "Python", "max_stars_repo_path": "backup/simulated_averaging_april22.py", "max_stars_repo_name": "SanaAwan5/edgecase_backdoors", "max_stars_repo_head_hexsha": "c892024242e45557fa94363ecadc355a9250bca0", "max_stars_repo_licenses":... |
# Copyright (c) 1996-2015 PSERC. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
"""Power flow data for 9 bus, 3 generator case.
Modifications:
1. Add 3 new lines to complicate the network
2. twice the loads
Additional data:
... | {"hexsha": "f88cfa25a30adfa3008406fdd55bd8a13a5a0257", "size": 4512, "ext": "py", "lang": "Python", "max_stars_repo_path": "Uncertainty/data/case-9-modified/case9.py", "max_stars_repo_name": "thanever/SOC", "max_stars_repo_head_hexsha": "9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import random,sys
import scipy
from scipy.spatial.distance import pdist,squareform,cdist
#from scipy.spatial import distance_matrix
import matplotlib.pyplot as plt
import scipy
### "for loop" version
### faster than "matrix version"
### because only need to consider points within h_k
### for loop... | {"hexsha": "264003e14799f041d8920a3bc23352775eb6833c", "size": 16290, "ext": "py", "lang": "Python", "max_stars_repo_path": "kmean_clustering/kmean_clustering.py", "max_stars_repo_name": "chvlyl/kernel_kmeans_and_adaptive_clustering", "max_stars_repo_head_hexsha": "0dd95158abc566b1975672016eaa327df8505267", "max_stars_... |
import json
import os
from collections import defaultdict
import cv2
import numpy as np
import torchvision.transforms as tf
from models.utils import draw_umich_gaussian, gaussian_radius, line_gaussian
from PIL import Image
from shapely.geometry import Polygon
from torch.utils import data
class SUNRGBD(... | {"hexsha": "3770de3f2f701ecc83ad00ca7e8efea6c5bd4b57", "size": 11895, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/sunrgbd.py", "max_stars_repo_name": "litsunshine/NonCuboidRoom", "max_stars_repo_head_hexsha": "c782222b951c622d80cae5f3217424dc2cbe6ef5", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python3
"""
Visualize a detector output on the CS6 validation set.
The val set GT annotations are in an FDDB/WIDER-style txt file format.
A symlink 'data/CS6' should point to the CS6 data root location
(on Gypsum this is in /mnt/nfs/scratch1/arunirc/data/CS6/CS6/CS6.0.01/CS6).
Usage (on slurm cluste... | {"hexsha": "158319cc51b6bfa9c8824295a0463bd652f37315", "size": 5215, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/face/viz_detector_cs6.py", "max_stars_repo_name": "AruniRC/detectron-self-train", "max_stars_repo_head_hexsha": "a5d0edc51aeab92b953948ef2401294e87efb719", "max_stars_repo_licenses": ["MIT"]... |
import os
from shutil import *
import random, math
import scipy.misc
import numpy as np
import tensorflow as tf
def clear_duplicated_layers(layers):
layers0 = [layers[0]]
for layer in layers:
if layer.name != layers0[-1].name:
layers0.append(layer)
return layers0
def allocate_gpu(gpu_i... | {"hexsha": "f1fb45f3687e0efe26e0857ab4340fd3e5eb8587", "size": 8894, "ext": "py", "lang": "Python", "max_stars_repo_path": "author_code_base/WGAN/common/utils.py", "max_stars_repo_name": "MichaelKonobeev/adashift", "max_stars_repo_head_hexsha": "bf86b021d42e922078a39246770f0f875300a6f3", "max_stars_repo_licenses": ["MI... |
import dash
import dash_bootstrap_components as dbc
from dash import dcc
from dash import html
from dash import dash_table
from dash.dependencies import Input, Output, State
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import base64
image_filename = 'cover.png'
... | {"hexsha": "64e0eda8eebca0be9d214d8b594a50791e8d4f0c", "size": 554, "ext": "py", "lang": "Python", "max_stars_repo_path": "image.py", "max_stars_repo_name": "daxinniu/data1050_dash_app", "max_stars_repo_head_hexsha": "451fe05ae56b6f7d1585d6f0e0526395b9b7f16d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import os
import pickle
import numpy as np
import json
def sortbylength(X, y) :
len_t = np.argsort([len(x) for x in X])
X1 = [X[i] for i in len_t]
y1 = [y[i] for i in len_t]
return X1, y1
def filterbylength(X, y, min_length = None, max_length = None) :
lens = [len(x)-2 for x in X]
min_l = ... | {"hexsha": "d13949a9f039ffee1ffea702b7c1eec6f1f4010f", "size": 5385, "ext": "py", "lang": "Python", "max_stars_repo_path": "Trainers/DatasetBC.py", "max_stars_repo_name": "wenting-zhao/cs6741_replication", "max_stars_repo_head_hexsha": "fbd8275793c5d2b097458c68bc3bba00144665aa", "max_stars_repo_licenses": ["MIT"], "max... |
import data.rat
open function
namespace mth1001
section composite
def q₁ (x : ℕ) : ℤ := x + 3
def q₂ (x : ℤ) : ℚ := 2 * x
/-
When a function `f` takes values from a type (or set) `α` and returns values in a type (or set) `β`,
we write that the *domain* of `f` is `α` and the *codomain* of `f` is `β`. This is denote... | {"author": "gihanmarasingha", "repo": "mth1001_tutorial", "sha": "bb277eebd5013766e1418365b91416b406275130", "save_path": "github-repos/lean/gihanmarasingha-mth1001_tutorial", "path": "github-repos/lean/gihanmarasingha-mth1001_tutorial/mth1001_tutorial-bb277eebd5013766e1418365b91416b406275130/src/exercises/src_32_compo... |
import tensorflow as tf
import numpy as np
import sys
import random
class GruRNN(object):
def __init__(self, num_classes, state_size, learning_rate=0.1, model_name='gru_rnn_model', ckpt_path='./ckpt/gru/'):
self.num_classes = num_classes
self.state_size = state_size
self.learning_rate = le... | {"hexsha": "54abfacce7ecc043bfc284fee0ef51570beb9ee9", "size": 5236, "ext": "py", "lang": "Python", "max_stars_repo_path": "rnn_from_scratch/rnn_units/gru_rnn.py", "max_stars_repo_name": "IsaacChanghau/AmusingPythonCodes", "max_stars_repo_head_hexsha": "013ecaaafe62696866b47b0910e1db00cca9ea37", "max_stars_repo_license... |
import numpy as np
def coll_func(x):
return (
0.25
+ (np.sqrt(3) / (4 * np.pi)) * np.log((x ** (1 / 3) + 1) ** 3 / (x + 1))
+ (3 / (2 * np.pi)) * np.arctan((2 * x ** (1 / 3) - 1) / (np.sqrt(3)))
)
def WE_SA_collection_eff(TYPE="PINE"):
coll_eff = []
if TYPE == "ALS":
... | {"hexsha": "befeebe1547b72570f64e9bfaae32ee37b66db85", "size": 1600, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/elchempy/experiments/EC_conditions/electrode.py", "max_stars_repo_name": "MyPyDavid/ECpy", "max_stars_repo_head_hexsha": "b74842b64eca86d2181067fdb22bfa8fa4b2c8bb", "max_stars_repo_licenses": ... |
import bpy
import mathutils as mut
import numpy as np
import operator
from collections import deque
from constants import C, ORIGIN, CustomError, D, EASE_IN_OUT, PI, WHITE,\
OBJECT_COUNTER, BLACK
from externals.blender_utils import selectOnly, computeQuaternion
from externals.bezier_interpolation import interpolate... | {"hexsha": "3543d22c1543ff8e9db0a97b6dc874552e6eca38", "size": 25892, "ext": "py", "lang": "Python", "max_stars_repo_path": "peeps/blobjects/blobject.py", "max_stars_repo_name": "hmomin/peeps", "max_stars_repo_head_hexsha": "885a48262872c9fa51616ae1987463936fd0f743", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import torch
from abs_models import utils as u
import numpy as np
def squared_L2_loss(a, b, axes, keepdim=True):
return u.tsum((a - b)**2, axes=axes, keepdim=keepdim)
def KLD(mu_latent_q, sig_q=1., dim=-3):
"""
:param mu_latent_q: z must be shape (..., n_latent ...) at i-th pos
:param sig_q: scalar... | {"hexsha": "853ff048cef441826c21e5187119df5284223302", "size": 2324, "ext": "py", "lang": "Python", "max_stars_repo_path": "abs_models/loss_functions.py", "max_stars_repo_name": "akashkumar25/AnalysisBySynthesis", "max_stars_repo_head_hexsha": "daa5a3df5c5fe2f809b6e2aa755b15ed3e07c3ff", "max_stars_repo_licenses": ["Apa... |
"""
Clase "Decaimiento radiactivo"
Luis Eduardo Sánchez González
Facultad de Ciencias Físico Matemáticas
Física Computacional
sáb 01 may 2021 10:12:14 CDT
Repositorio: https://github.com/Luis2501/Fisica-Computacional-1
"""
from random import random
import numpy as np
class Radioactive_Decay:
def __init__(self... | {"hexsha": "4de1b2032e7cc5180712f94077f5f2e386fe2aab", "size": 776, "ext": "py", "lang": "Python", "max_stars_repo_path": "Procesos aleatorios/Radioactive_Decay.py", "max_stars_repo_name": "Luis2501/Fisica-Computacional-1", "max_stars_repo_head_hexsha": "502481ad900cf7dabdb7f9a74b0f68c60876c5ce", "max_stars_repo_licens... |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 15 11:24:15 2021
@author: Christian Pfister
https://cpfister.com
https://github.com/christianpfister43?tab=repositories
Schuldenuhr: https://www.gold.de/staatsverschuldung-deutschland/
"""
import numpy as np
from PIL import ImageGrab
import cv2
import os
#%% set your c... | {"hexsha": "894ba525fc76b93ec5a0b50dcd0042cae65d7f6d", "size": 1866, "ext": "py", "lang": "Python", "max_stars_repo_path": "screen_parsing_schuldenuhr.py", "max_stars_repo_name": "christianpfister43/Reading-a-Live-Ticker", "max_stars_repo_head_hexsha": "46eef58c69dbcd805c32646b38b56efecbde5d4e", "max_stars_repo_license... |
#app.py
from flask import Flask, flash, request, redirect, url_for, render_template
import urllib.request
from werkzeug.utils import secure_filename
import cv2
import pytesseract
import numpy as np
app = Flask(__name__)
UPLOAD_FOLDER = 'static/uploads/'
app.secret_key = "secret key"
app.config['UPLO... | {"hexsha": "d2cb862ffe38e6d5c023e5a68319d44cf77f72d8", "size": 4489, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "longtp12/opencv4nodejs", "max_stars_repo_head_hexsha": "05952d1119c1dd9ccf696434bcc5cdf73f06b627", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
chapter\<open>Preliminaries\<close>
text\<open>In this chapter, we introduce the preliminaries, including a three-valued logic, variables,
arithmetic expressions and guard expressions.\<close>
section\<open>Three-Valued Logic\<close>
text\<open>Because our EFSMs are dynamically typed, we cannot rely on conventional B... | {"author": "jmafoster1", "repo": "efsm-isabelle", "sha": "fde322562b98c9b4618c112e36a6ac5b9a056610", "save_path": "github-repos/isabelle/jmafoster1-efsm-isabelle", "path": "github-repos/isabelle/jmafoster1-efsm-isabelle/efsm-isabelle-fde322562b98c9b4618c112e36a6ac5b9a056610/Trilean.thy"} |
"""
4차원 데이터를 2차원으로 변환한 후에 max pooling 구현
"""
import numpy as np
from common.util import im2col
if __name__ == '__main__':
np.random.seed(116)
# 가상의 이미지 데이터(c,h,w) = (3,4,4) 1개를 난수로 생성 -> (1,3,4,4)
x = np.random.randint(10, size=(1, 3, 4, 4))
print(x, 'shape:', x.shape)
# 4차원 데이터를 2차원 ndarray로 변환... | {"hexsha": "66b3b4c5839e4b3791679cbcec1b4a2288ee9657", "size": 830, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch07/ex11_pooling.py", "max_stars_repo_name": "lee-hyeonseung/lab_dl", "max_stars_repo_head_hexsha": "b8906247b6e0e2586f538081e2efaf47dac34972", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import subprocess
import click
import numpy as np
import fitsio
import esutil.numpy_util
import sep
from lsst.daf.persistence import Butler
from sxdes import run_sep
from ssi_tools.layout_utils import make_hexgrid_for_tract
from fsi_tools.matching import do_balrogesque_matching
from desc_dc2_dm_data import... | {"hexsha": "b8a95685409aa11e505ea51cdc0549a0c037b275", "size": 6766, "ext": "py", "lang": "Python", "max_stars_repo_path": "2020_07_16_coadd_sep/process_tract_patch.py", "max_stars_repo_name": "LSSTDESC/ssi-cosmodc2", "max_stars_repo_head_hexsha": "678e4f708018e492f2232b8e5cd3a7845341c5f0", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma analz_insert_MPair [simp]:
"analz (insert \<lbrace>X,Y\<rbrace> H) =
insert \<lbrace>X,Y\<rbrace> (analz (insert X (insert Y H)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. analz (insert \<lbrace>X, Y\<rbrace> H) = insert \<lbrace>X, Y\<rbrace> (analz (insert X (insert Y H)))
... | {"llama_tokens": 975, "file": null, "length": 6} |
# -*- coding: utf-8 -*-
"""
============================================================================
Authors:
Edwin Alvarez-Mamani and Jose Luis Soncco-Alvarez*
*Department of Informatics
Universidad Nacional de San Antonio Abad del Cusco (UNSAAC) - Perú
=========================================================... | {"hexsha": "7ba507e2f7051b1a2e1f3de2b40561ee6c497dac", "size": 1797, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphic_convergence_topology_v2.py", "max_stars_repo_name": "win7/parallel_social_spider_optimization", "max_stars_repo_head_hexsha": "9dbad144e4242fef2ff6aacc8e72376e14b03a61", "max_stars_repo_li... |
The address(rifle range, 38.5361, 121.7508) behind King Hall hasnt been a rifle range for years. It currently houses some of the business office for Facilities Management. There is an almostcompletely faded RIFLE RANGE sign above the door (youll have to look very closely!)
At one point this was the ROTC rifle range... | {"hexsha": "d02c6cab441888b5d59a88672313e38a9be75de2", "size": 754, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Rifle_Range.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
+incdir+./
+incdir+../../
FPU_F32_ADD.sv
FPU_F32_DIV.sv
FPU_F32_MUL.sv
FPU_F32_to_INT.sv
FPU_INT_to_F32.sv
top.sv
| {"hexsha": "9b080d07b2bc847003b4de20278cb2829101ce35", "size": 115, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Meitner/System/HDL/DUTs/FPU/top.f", "max_stars_repo_name": "testdrive-profiling-master/profiles", "max_stars_repo_head_hexsha": "6e3854874366530f4e7ae130000000812eda5ff7", "max_stars_repo_licenses"... |
[STATEMENT]
lemma mapCollect_const[simp]:
"m \<noteq> Map.empty \<Longrightarrow> {e | k\<mapsto>v\<in>m} = {e}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. m \<noteq> Map.empty \<Longrightarrow> {e |k\<mapsto>v\<in>m} = {e}
[PROOF STEP]
unfolding mapCollect_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1.... | {"llama_tokens": 186, "file": "Launchbury_AList-Utils", "length": 2} |
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import division
from __future__ import print_function
import numpy as np
import os
from os.path import join
import torch
import pandas as pd
import scipy.sparse as sp
from scipy.sparse import coo_matrix
from torch.utils.data... | {"hexsha": "c79941be7c198e2be99c933c77a5d0e59104cff6", "size": 3949, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/gat/data_loader.py", "max_stars_repo_name": "awesome-archive/OAG", "max_stars_repo_head_hexsha": "551a237e8aa1fd6642b6c89f0fdb545104c09712", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// file: val3_fstream_socket.cpp, style: indent -kr -ci2 -cli2 -i2 -l130 -nut <file>
//
// License http://opensource.org/licenses/BSD-3-Clause
// Copyright (c) 2016 14U2g4ocMy5aB2cY4cmCtbXD6qyNQzujuA (serves donations as well)
// All rights reserved.
//
// assembles string that flows over topic to robot control in VAL3... | {"hexsha": "22bfc95bb32aa243e7015d01401e992a0ba00cd1", "size": 8957, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dotnect_platform/src/gen_val3_class.cpp", "max_stars_repo_name": "vwas2/Dotnet_stack", "max_stars_repo_head_hexsha": "77edf5eb3dbea98c1a7c43868b435d862e8058d9", "max_stars_repo_licenses": ["BSD-3-Cl... |
'''
Created on 12 Aug 2020
@author: Tobias Pielok
'''
import numpy as np
from sklearn.decomposition import TruncatedSVD
from scipy.linalg import expm
from scipy.linalg import logm
from typing import List, Tuple
def svd_dmd(ts: np.array, r: int) -> Tuple[np.array, np.array]:
'''
Returns the SVD-DMD of ts.
... | {"hexsha": "ca1c26917c6f99c0beb01addb70f57a24e66c439", "size": 9278, "ext": "py", "lang": "Python", "max_stars_repo_path": "PKRL/svd_dmd.py", "max_stars_repo_name": "pkmtum/Probabilistic_Koopman_Learning", "max_stars_repo_head_hexsha": "e91730533e53f897221c3b7a62116a20f45eb59b", "max_stars_repo_licenses": ["MIT"], "max... |
*DECK DGMRES
SUBROUTINE DGMRES(N, B, X, NELT, IA, JA, A, ISYM, MATVEC, MSOLVE,
$ ITOL, TOL, ITMAX, ITER, ERR, IERR, IUNIT, SB, SX,
$ RGWK, LRGW, IGWK, LIGW, RWORK, IWORK )
C***BEGIN PROLOGUE DGMRES
C***DATE WRITTEN 890404 (YYMMDD)
C***REVISION DATE 890404 (YYMMDD)
C***CATEGORY NO. D2A4... | {"hexsha": "b159b60d497a9d0e2039e2d11ae407d924bf626e", "size": 118170, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "models/glc/cism/glimmer-cism/libglimmer-solve/SLAP/dgmres.f", "max_stars_repo_name": "fmyuan/clm-microbe", "max_stars_repo_head_hexsha": "9faee9ed7d6c092c4a9e4a207f32cbffab78b85c", "max_stars_re... |
import pandas as pd
import numpy as np
from sklearn.model_selection import GridSearchCV
class modelSelection:
def __init__(self, models, params):
if not set(models.keys()).issubset(set(params.keys())):
missing_params = list(set(models.keys()) - set(params.keys()))
raise ValueError... | {"hexsha": "d0cb214e861d0e7c72e5c382f774b6ca9a76f0c9", "size": 2615, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_libs/model_selection.py", "max_stars_repo_name": "SamTube405/MCAS", "max_stars_repo_head_hexsha": "bc6500ad509f798fa3d60f7c3f436e6b30eda5f8", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from setuptools import setup
from setuptools import Extension
import numpy as np
import os
from Cython.Build import cythonize
sourcefiles = ['gmmmc/fastgmm/fast_likelihood.pyx']
ext_modules = [Extension("fast_likelihood",
sourcefiles,
include_dirs = [np.get_include()... | {"hexsha": "0658e0552ad62024207a5ef200283e241ecebc62", "size": 1120, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "jeremy-ma/gmmmc", "max_stars_repo_head_hexsha": "fe2c58d5263e78ed360a84fcb85a5d6e08da0d70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
[STATEMENT]
lemma r01_binary_expression_ex1:
assumes "0 < r" "r < 1"
shows "\<exists>i. r01_binary_expansion' r i = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>i. r01_binary_expansion' r i = 1
[PROOF STEP]
proof (rule ccontr)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<nexists>i. r01_bina... | {"llama_tokens": 1827, "file": "Quasi_Borel_Spaces_StandardBorel", "length": 22} |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "3a383d30e5f4bc184854dcc81164eb47316c0826", "size": 2037, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/contrib/test_hexagon/test_run_unit_tests.py", "max_stars_repo_name": "LEA0317/incubator-tvm", "max_stars_repo_head_hexsha": "de21c8f2ef507587fdcc99b851404de5aeeb5a16", "max_stars_repo... |
"""Information Retrieval metrics
Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning ... | {"hexsha": "f130e393d95c7597cb84ec5c5fd03d2bf8a6c3f9", "size": 6532, "ext": "py", "lang": "Python", "max_stars_repo_path": "ninjia/preprocess/metrics/rank_metrics.py", "max_stars_repo_name": "taohu88/ninjia", "max_stars_repo_head_hexsha": "43e68534aa3a446b237c5dce757c02b41b2e923b", "max_stars_repo_licenses": ["MIT"], "... |
# coding: utf-8
import sys, os
sys.path.append(os.pardir)
import pickle
import numpy as np
from collections import OrderedDict
from common.layers import *
from common.gradient import numerical_gradient
from common.util import *
def he_stdev(node_num):
return np.sqrt(2)/np.sqrt(node_num)
class ConvNet:
"""Conv... | {"hexsha": "5a6104c2633437c2cb141d711bc611dcddca702d", "size": 9402, "ext": "py", "lang": "Python", "max_stars_repo_path": "LEGACY/custom_convnet.py", "max_stars_repo_name": "YeongHyeon/Convolution_Neural_Network", "max_stars_repo_head_hexsha": "11b75f011078c741a5ba1b935c3ede1397b7b46d", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import os.path as osp
from glob import glob
import re
import argparse
import collections
import tensorflow.compat.v1 as tf
#import tensorflow as tf
import cv2
import ... | {"hexsha": "83922bac802398ab2dffc8871dfcfe96f12579a5", "size": 22333, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/tracking.py", "max_stars_repo_name": "tongtybj/SiamFC-TensorFlow", "max_stars_repo_head_hexsha": "b885c62132fb6203e820570a2263e6a401af5d26", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# ------------------------------------------------------------------------------
# Copyright 2020 Forschungszentrum Jülich GmbH and Aix-Marseille Université
# "Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements; and to You under the Apache License,
# Version 2.0. "
#
# ... | {"hexsha": "c4f9cbcebdf4f540e137b9c4dc56d8d0e9468f80", "size": 6868, "ext": "py", "lang": "Python", "max_stars_repo_path": "refactored_modular/wrapper/elephant_wrapper_files/Rate_to_spike.py", "max_stars_repo_name": "mfahdaz/EBRAINS-InterscaleHUB", "max_stars_repo_head_hexsha": "c8c3952bfdf5f5cd913b7b078514b9a80829bd4d... |
theory proof_insert
imports
LLRB_SET
LLRB_IMP
begin
subsection \<open>proof of bst_insert\<close>
lemma bst_paint: "inorder(paint c t) = inorder t"
by(induct t)
auto
lemma bst_rightredB:
"inorder (rightredB l a r) = inorder l @ a # inorder r"
by(cases "(l, a, r)" rule: rightredB.cases) auto
lemma bst... | {"author": "Criank", "repo": "LLRB_PROOF_NEW", "sha": "2991cfdeee0ef0ce6b2992c393ab61443885781b", "save_path": "github-repos/isabelle/Criank-LLRB_PROOF_NEW", "path": "github-repos/isabelle/Criank-LLRB_PROOF_NEW/LLRB_PROOF_NEW-2991cfdeee0ef0ce6b2992c393ab61443885781b/proof_insert.thy"} |
library(base)
library(caret)
library(cluster)
library(dummies)
library(e1071)
library(factoextra)
library(modules)
library(RSNNS)
library(rstudioapi)
library(stats)
library(tidyverse)
library(utils)
base::setwd(base::dirname(rstudioapi::getActiveDocumentContext()$path))
start <- base::Sys.time()
base::set.seed(0xACD... | {"hexsha": "bad03cca8624bb7e89420fc1d26c703cc67b6ac9", "size": 2497, "ext": "r", "lang": "R", "max_stars_repo_path": "src/clustering.r", "max_stars_repo_name": "7Rocky/phishing-detection", "max_stars_repo_head_hexsha": "65f96bbf5751ee3ae1eab8f029797030678e956f", "max_stars_repo_licenses": ["0BSD"], "max_stars_count": 1... |
# Python modules
# 3rd party modules
import numpy as np
def cross_correlate(x, y, lag, covariance=False):
"""
This method calculates the cross correlation Pxy(lag) or cross covariance
Rxy(lag) of two data sets x and y as a function of the lag.
x: a numpy array of type integer, float or complex.... | {"hexsha": "e94aa4f39abe210a32aed47e3d34261f3ffe6c58", "size": 1512, "ext": "py", "lang": "Python", "max_stars_repo_path": "vespa/analysis/algos/cross_correlate.py", "max_stars_repo_name": "vespa-mrs/vespa", "max_stars_repo_head_hexsha": "6d3e84a206ec427ac1304e70c7fadf817432956b", "max_stars_repo_licenses": ["BSD-3-Cla... |
# CENG 487 Assignment4 by
# Arif Burak Demiray
# December 2021
from OpenGL.GL import *
from OpenGL.GLUT.fonts import GLUT_BITMAP_9_BY_15
from OpenGL.raw.GLUT import glutBitmapCharacter
from numpy import character
def gluPrintText(text: 'list[character]', position_y: int = 0) -> None:
"""
Helper method to pri... | {"hexsha": "034e2e62144fe7c7512d48ea2ec8171a132b5fb3", "size": 504, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/glu_helper.py", "max_stars_repo_name": "arifBurakDemiray/computer-graphics", "max_stars_repo_head_hexsha": "acf4781f92e325b12d986974c448b0e3520af431", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import cv2
from .vector.vector2d import Vector2D
from .features import Features
class FacialLandmarks68Index(object):
POINT_OF_SIGHT = 27
RIGHT_EYE_CORNER = 36
LEFT_EYE_CORNER = 45
NOSE = 30
MOUTH_UP = 51
MOUTH_DOWN = 57
MOUTH_UP = 51
RIGHT_MOUTH_CORNER = 48
LEFT... | {"hexsha": "92dfe4de6351572eecb2a9d1c075cabc8fc5011f", "size": 2409, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyuwds3/types/landmarks.py", "max_stars_repo_name": "uwds3/uwds3", "max_stars_repo_head_hexsha": "3ec70111d63db0c8d97d9f1e0110b7fe9ad56179", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 8 13:54:17 2019
@author: wmy
"""
import scipy
import tensorflow as tf
from keras.datasets import mnist
from keras import backend as K
from keras import layers
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from keras.layers im... | {"hexsha": "ae73957711a35d054184c1f5823e4a6a2c522bf3", "size": 5090, "ext": "py", "lang": "Python", "max_stars_repo_path": "facerec.py", "max_stars_repo_name": "wmylxmj/Face-Recognition", "max_stars_repo_head_hexsha": "ee461d179d4c25ec0292ce280738f60352e99be7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, ... |
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE order_book_tests
#include <boost/test/unit_test.hpp>
#include "order_book.h"
BOOST_AUTO_TEST_CASE( test_buy_ordering )
{
ae::order_book book;
book.insert(ae::order("A", "AUDUSD", 100, 10));
book.insert(ae::order("A", "AUDUSD", 100, 7));
book.insert(ae... | {"hexsha": "198564f26c8a04b89c4f8302fc688deceda7fb92", "size": 3394, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "part_1/cpp_solution/order_book_tests.cpp", "max_stars_repo_name": "jessmorecroft/Exchange", "max_stars_repo_head_hexsha": "7b190b22304c6381db22b7722f0ce4518de3df2c", "max_stars_repo_licenses": ["MIT... |
<a href="https://colab.research.google.com/github/mella30/Deep-Learning-with-Tensorflow-2/blob/main/Course3-Probabilistic_Deep_Learning_with_Tensorflow2/week4_KL_divergence.ipynb" target="_parent"></a>
# Kullback-Leibler divergence
This reading will review the definition of the Kullback-Leibler (or KL) divergence, lo... | {"hexsha": "81c303fe26569349ade5fb9663ed60be0c92e3dd", "size": 100221, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Course3-Probabilistic_Deep_Learning_with_Tensorflow2/week4_KL_divergence.ipynb", "max_stars_repo_name": "mella30/Probabilistic-Deep-Learning-with-TensorFlow-2", "max_stars_repo_head... |
import gurobipy as gp
from gurobipy import GRB
from mosek.fusion import *
import time,sys
import numpy as np
import pandas as pd
from scipy.linalg import sqrtm
from DimacsReader import *
def save(name,finished,value,relax,soltime,iteration,innner, xsol):
f = open("../output/Application2/"+name+"/InnerOuterApproxA... | {"hexsha": "d5d4bf048809b4c6a14acd2499d2055e1895744b", "size": 13531, "ext": "py", "lang": "Python", "max_stars_repo_path": "AllPythonFiles/Application2_InnerOuterApproxAlgo.py", "max_stars_repo_name": "aoustry/SIP-with-QP-LL", "max_stars_repo_head_hexsha": "afa7b18f6dcb98c04db4786ec5fcdf0607824191", "max_stars_repo_li... |
* MB05OD EXAMPLE PROGRAM TEXT
* Copyright (c) 2002-2020 NICONET e.V.
*
* .. Parameters ..
INTEGER NIN, NOUT
PARAMETER ( NIN = 5, NOUT = 6 )
INTEGER NMAX
PARAMETER ( NMAX = 20 )
INTEGER LDA
PARAMETER ( LDA = NMAX )
INTE... | {"hexsha": "fd72a67eba3e843308081b286487324d86b9a41e", "size": 2264, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "examples/TMB05OD.f", "max_stars_repo_name": "bnavigator/SLICOT-Reference", "max_stars_repo_head_hexsha": "7b96b6470ee0eaf75519a612d15d5e3e2857407d", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
[STATEMENT]
lemma plus_pres_lens_indep' [simp]:
"\<lbrakk> X \<bowtie> Y; X \<bowtie> Z \<rbrakk> \<Longrightarrow> X \<bowtie> Y +\<^sub>L Z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>X \<bowtie> Y; X \<bowtie> Z\<rbrakk> \<Longrightarrow> X \<bowtie> Y +\<^sub>L Z
[PROOF STEP]
by (auto intro: lens_... | {"llama_tokens": 159, "file": "Optics_Lens_Algebra", "length": 1} |
------------------------------------------------------------------------
-- Indexed applicative functors
------------------------------------------------------------------------
-- Note that currently the applicative functor laws are not included
-- here.
module Category.Applicative.Indexed where
open import Data.Fu... | {"hexsha": "ba21606725126670a2d8eacc4992bda2531c2a5c", "size": 1099, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "vendor/stdlib/src/Category/Applicative/Indexed.agda", "max_stars_repo_name": "isabella232/Lemmachine", "max_stars_repo_head_hexsha": "8ef786b40e4a9ab274c6103dc697dcb658cf3db3", "max_stars_repo_lic... |
function [X,J,dXdx,dXdxI]=JacobiDG2(DG,F,Topo,Param)
ksi=DG.xwX;
eta=DG.xwY;
nX=DG.OrdPolyX+1;
nY=DG.OrdPolyY+1;
X=zeros(nX,nY,3);
dXdx=zeros(nX,nY,2,2);
dXdxI=zeros(nX,nY,2,2);
J=zeros(nX,nY);
for j=1:nY
for i=1:nX
X(i,j,1:2)=0.25*((1-ksi(i))*(1-eta(j))*F.P(1:2,1)...
+(1+ksi(i))*(1-eta(j))*F.... | {"hexsha": "9881329194f562f31bb52ca34199db37ab814257", "size": 784, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Grid/JacobiDG2.jl", "max_stars_repo_name": "CliMA/CGDycore.jl", "max_stars_repo_head_hexsha": "77297631f8db7775f19daee2d7ac75bc810d9c11", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
#
# file: GWO.py
#
# Grey wolf optimization
#
# RTK, 23-Dec-2019
# Last update: 26-May-2020
#
################################################################
import numpy as np
################################################################
# GWO
#
class GWO:
"""Grey wolf optimization"""
#----------... | {"hexsha": "745f8ba64ad21ed255b54c25435a65a5bac03fdf", "size": 6728, "ext": "py", "lang": "Python", "max_stars_repo_path": "appendix/GWO.py", "max_stars_repo_name": "rkneusel9/StrangeCodeBook", "max_stars_repo_head_hexsha": "70ed93396885a5cbf2f4d774d9aa30feca83e46d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
theory State_Monad_EX
imports
Main
"State_Monad_HL"
begin
record S1 =
x_S1:: int
y_S1:: int
z_S1:: int
(* update functions *)
definition x_S1u:: "S1 \<Rightarrow> int \<Rightarrow> S1" where "x_S1u s v = s \<lparr> x_S1 := v \<rparr>"
definition y_S1u:: "S1 \<Rightarrow> int \<Rightarrow> S1" where "y_S1u... | {"author": "SimplisticCode", "repo": "Tarjan-Isabelle", "sha": "ecd72ef5fc352075e6037965cc30844b7db4bacc", "save_path": "github-repos/isabelle/SimplisticCode-Tarjan-Isabelle", "path": "github-repos/isabelle/SimplisticCode-Tarjan-Isabelle/Tarjan-Isabelle-ecd72ef5fc352075e6037965cc30844b7db4bacc/Monad_Play Around/State_M... |
#!/usr/bin/python3.6
import sys
import json
import numpy as np
import math
from dask.distributed import Client
import shutil
problem_instance_file = sys.argv[1]
D = np.genfromtxt (problem_instance_file, delimiter=",")
shutil.copyfile(problem_instance_file, '/dev/shm/D.csv')
# Now compute our solution
import pyrank... | {"hexsha": "bb98aeaf52b932f7be3f8ed55032d3ab90b68cc8", "size": 732, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/scripts/pruning_paper_wrapper_dask.py", "max_stars_repo_name": "IGARDS/ranklib", "max_stars_repo_head_hexsha": "1acd8c0bd4d4045b55e6c5bd6cbb2fbe080c7479", "max_stars_repo_licenses": ["MI... |
// ---------------------------------------------------------------------------|
// Boost Test Framework
// ---------------------------------------------------------------------------|
#include <boost/test/unit_test.hpp>
// ---------------------------------------------------------------------------|
// Standard include... | {"hexsha": "7d6cf41f56338b8d8417c61fee0db8b8970c34a3", "size": 7067, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OpenYuma/netconf/test/test-suites/system/simple-edit-tests-confirmed-commit.cpp", "max_stars_repo_name": "5GExchange/escape", "max_stars_repo_head_hexsha": "eb35d460597a0386b18dd5b6a5f62a3f30eed5fa"... |
[STATEMENT]
lemma resCasesB[consumes 2, case_names Open Res]:
fixes x :: name
and P :: pi
and a :: name
and y :: name
and RP' :: pi
assumes Trans: "<\<nu>y>P \<longmapsto> a<\<nu>x> \<prec> RP'"
and xineqy: "x \<noteq> y"
and rcOpen: "\<And>P'. \<lbrakk>P \<longmapsto>(OutputR a y) \... | {"llama_tokens": 24273, "file": "Pi_Calculus_Early_Semantics", "length": 132} |
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef CellB... | {"author": "canlab", "repo": "CanlabCore", "sha": "af242e120f0480c4feaeea90471c015a14f1f60e", "save_path": "github-repos/MATLAB/canlab-CanlabCore", "path": "github-repos/MATLAB/canlab-CanlabCore/CanlabCore-af242e120f0480c4feaeea90471c015a14f1f60e/CanlabCore/External/umap/util/CellBasics.m"} |
(* This file is generated by Why3's Coq driver *)
(* Beware! Only edit allowed sections below *)
Require Import BuiltIn.
Require BuiltIn.
Require HighOrd.
Require int.Int.
Require int.Abs.
Require int.EuclideanDivision.
Require list.List.
Require list.Length.
Require list.Mem.
Require map.Map.
Require bool.Bool.
Req... | {"author": "williameriksson", "repo": "compiler_construction", "sha": "f71ab5ab1af29c1cb5ebc2b3bc3d1dc6bba609b9", "save_path": "github-repos/coq/williameriksson-compiler_construction", "path": "github-repos/coq/williameriksson-compiler_construction/compiler_construction-f71ab5ab1af29c1cb5ebc2b3bc3d1dc6bba609b9/why3/com... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 12 08:27:36 2019
@author: yaz
"""
from numpy import *
from scipy.integrate import odeint
from scipy.optimize import curve_fit, least_squares
class moments:
def __init__(self, a=None, b=None, la=None, alpha_a=None, alpha_i=None, sigma=None, bet... | {"hexsha": "6a091c476a5d0a3c29ab6665a563916be15f319e", "size": 16800, "ext": "py", "lang": "Python", "max_stars_repo_path": "dynamo/tools/utils_moments_deprecated.py", "max_stars_repo_name": "softbear/dynamo-release", "max_stars_repo_head_hexsha": "18b64d257c755ccb3aedd7877d9d39f8c40f46fa", "max_stars_repo_licenses": [... |
import numpy as np
from PIL import Image
def matched_tiling(img, block_size, target_shape, overlap_size):
new_block_size = block_size - overlap_size
n_blocks = (np.ceil(np.true_divide(target_shape[0:2], new_block_size))).astype('uint32')
output = np.zeros(target_shape, 'uint8')
print("Total ... | {"hexsha": "cfc89ff984c71f6eded4721e22b3dfbbdd61f4af", "size": 7110, "ext": "py", "lang": "Python", "max_stars_repo_path": "quilt/tile/matched_tiling.py", "max_stars_repo_name": "deep110/Quilter", "max_stars_repo_head_hexsha": "f5e8da986558acb7d2689c6e6400cc26c844684e", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#
# This file is a part of MolecularGraph.jl
# Licensed under the MIT License http://opensource.org/licenses/MIT
#
@testset "graph.triangle" begin
@testset "triangles" begin
graph1 = pathgraph(5)
@test isempty(triangles(graph1))
graph2 = plaingraph(5, [(1, 2), (2, 3), (3, 1)])
@test issetequal(collect... | {"hexsha": "26a8f067461a2d93052510d5ab5281819ac8950e", "size": 729, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/graph/triangle.jl", "max_stars_repo_name": "hhaensel/MolecularGraph.jl", "max_stars_repo_head_hexsha": "c54ccdf09274e36ed3d866604f99b497a39bfaf5", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import os
import cv2
import json
import sys
import numpy as np
from dataset_utils.utils import FolderVideoReader
from dataset_utils.diamond_accumulator import Accumulator
element_small = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
element_big = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
element_l... | {"hexsha": "1017a5ab9c6c3072022a666724e5a7f73099a6a5", "size": 9067, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset_utils/calib.py", "max_stars_repo_name": "kocurvik/retinanet_traffic_3D", "max_stars_repo_head_hexsha": "592ceac767750c65bb3d6678b36e6880a7bb0403", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np
import matplotlib.pyplot as plt
from typing import List, Tuple
from pegasusio import read_input, UnimodalData
from . import estimate_background_probs, demultiplex
def down_sampling(rna_gt: UnimodalData, hto_gt: UnimodalData, probs: List[float], n_threads: int = 1):
f = np.vectorize(lambda x, p... | {"hexsha": "733e7fb1b6a05c4503424d445da432ca0b669457", "size": 1927, "ext": "py", "lang": "Python", "max_stars_repo_path": "demuxEM/tools/down_sampling.py", "max_stars_repo_name": "slowkow/demuxEM", "max_stars_repo_head_hexsha": "6c2b851b25dc30633e5abfa57d687e31b25a7f16", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import numpy as np
from pycompss.api.api import compss_wait_on
from pycompss.api.constraint import constraint
from pycompss.api.parameter import COLLECTION_IN, COLLECTION_OUT, \
Type, Depth
from pycompss.api.task import task
from scipy.sparse import issparse
from scipy.sparse import vstack as vstack_sparse
from skl... | {"hexsha": "2178c258c3f808eda5c604bf765786d181f164e6", "size": 15311, "ext": "py", "lang": "Python", "max_stars_repo_path": "dislib/cluster/dbscan/base.py", "max_stars_repo_name": "alexbarcelo/dislib", "max_stars_repo_head_hexsha": "989f81f235ae30b17410a8d805df258c7d931b38", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import argparse
from datetime import datetime
from dateutil.relativedelta import relativedelta
import json
import numpy as np
import os
import pandas as pd
import scipy.stats
from subprocess import call
import sys
parser = argparse.ArgumentParser(
description="""Join existing demographic and health data file w... | {"hexsha": "a1e68197e8bcd47daf235463656e275554fc3754", "size": 4020, "ext": "py", "lang": "Python", "max_stars_repo_path": "addNewHES.py", "max_stars_repo_name": "kasbohm/ukb_download_and_prep_template", "max_stars_repo_head_hexsha": "ef8c0171cb0f61d52b4dacf5edf2270ec42a48a1", "max_stars_repo_licenses": ["MIT"], "max_s... |
! Calculate PDF of a scalar field.
subroutine calc_pdf(dist_mf, lo, hi, ng, num_bins, bin_edges, bin_count, bin_x_sum)
use, intrinsic :: iso_c_binding
implicit none
integer(c_int), intent(in) :: lo(3), hi(3), ng
real(c_double), intent(in) :: dist_mf (lo(1)-ng:hi(1)+ng, lo(2)-ng:hi(2)+ng, lo(3)-ng:hi(3... | {"hexsha": "b48a7b0f0052b1a3e1be79cb01beb33a60af1903", "size": 1528, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "calc_pdf.f90", "max_stars_repo_name": "bcfriesen/gimlet", "max_stars_repo_head_hexsha": "153771cfb5bf810e4f1ca9a8ec4549935a51320a", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], "max_stars_c... |
# Latent class models with one (loglinear independence) to three classes
data(election)
f <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG,
MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~1
nes1 <- poLCA(f,election,nclass=1) # log-likelihood: -18647.31
nes2 <- poLCA(f,election,nclass=2) # log-likelihood: -17344.9... | {"hexsha": "151747b7a21aaaa70a90372b80052e62b87e04e2", "size": 1047, "ext": "r", "lang": "R", "max_stars_repo_path": "poLCA/poLCA_election_demo.r", "max_stars_repo_name": "matthew9602/R-demo", "max_stars_repo_head_hexsha": "301e343750b6e6874985d6db9770379443ff3e86", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import torch
import torch.optim.lr_scheduler as lr_scheduler
from utils import lr_scheduler_ext, stacked_dict
from torch import nn
import pickle
import numpy as np
from collections import defaultdict
import importlib
from utils import WrappedSummaryWriter
import time
def debug_gradients_tbx(logger, config, net, ep... | {"hexsha": "aff2689b5f0f4242bc6754ef03d72261fb53de9d", "size": 14614, "ext": "py", "lang": "Python", "max_stars_repo_path": "runners/default_runner.py", "max_stars_repo_name": "VickyChing/ICASSP19", "max_stars_repo_head_hexsha": "247b0b9d738679f5472dd74f52bd1933b871e87f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python
# *-----------------------------------------------------------------------*
# | |
# | Copyright (c) 2013 by Paul Scherrer Institute (http://www.psi.ch) |
# | ... | {"hexsha": "e989c8c431717e66566a1044b537800d87cbb54c", "size": 10962, "ext": "py", "lang": "Python", "max_stars_repo_path": "h5pyViewerLib/hdfGrid.py", "max_stars_repo_name": "Eothred/h5pyViewer", "max_stars_repo_head_hexsha": "bba7ad3e1b7cf0896332afb8ebc064fe46ada70f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
import os
import pickle
import torch
import numpy as np
from math import ceil
from model_vc import Generator
ckpt_path = 'logs_dir/autovc_one_hot146000.ckpt'
conversion_list_path = 'conversion_list.txt'
data_dir = '../AutoVC_hujk17/full_106_spmel_nosli'
speaker_id_dict_path = '../AutoVC_hujk17/full_106_spm... | {"hexsha": "231659671eade845fbbf25bc538f110331da32c6", "size": 2882, "ext": "py", "lang": "Python", "max_stars_repo_path": "conversion.py", "max_stars_repo_name": "ruclion/AutoVC_one_hot", "max_stars_repo_head_hexsha": "db400a224b2e4544a480e0ab3f62b51b570d378c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# Return the index of the maximum entry of a given vector transformed by f.
Base.argmax(f::Any, v::AbsVec) = argmax(f, v, 1:length(v))
# Among specified indices, return the index of the maximum entry of a given vector
# transformed by f.
function Base.argmax(f::Any, v::AbsVec, indv::AbsVecInteger)
ind = 0 # retur... | {"hexsha": "f3093db6640b4a3e4808222bdc1d85989ae2f4dd", "size": 1081, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util.jl", "max_stars_repo_name": "wsshin/SALTBase.jl", "max_stars_repo_head_hexsha": "7e649196ebe80045e17a3227280011fb3fab1cb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_s... |
import gc
import os
from argparse import Namespace
from timeit import default_timer as timer
from typing import Union
import numpy as np
import pandas as pd
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from termcolor import colored
from mimic import log
from mimic.run_epochs import... | {"hexsha": "7b823463d44bcd38071e619783e3f837afd284a3", "size": 5288, "ext": "py", "lang": "Python", "max_stars_repo_path": "mimic/main_mimic.py", "max_stars_repo_name": "Jimmy2027/MoPoE-MIMIC", "max_stars_repo_head_hexsha": "d167719b0dc7ba002b7421eb82a83e47d2437795", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <boost/circular_buffer.hpp>
#include <iostream>
#include <fstream>
#include <sstream>
#include <algorithm>
#include <range/v3/algorithm.hpp>
#include <range/v3/numeric.hpp>
#include <range/v3/view.hpp>
namespace views = ranges::views;
int main(int argc, char **argv)
{
if (argc > 1) {
std::ifstream ifs(a... | {"hexsha": "4e458759d26deebe795d4939bb6305e76105278c", "size": 728, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "aoc2017/aoc170201.cpp", "max_stars_repo_name": "jiayuehua/adventOfCode", "max_stars_repo_head_hexsha": "fd47ddefd286fe94db204a9850110f8d1d74d15b", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
import re
import csv
import string
import numpy as np
from nltk.corpus import wordnet
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import WordPunctTokenizer
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from s... | {"hexsha": "e18dafc0048a48d570760a126b94555c114b9962", "size": 4437, "ext": "py", "lang": "Python", "max_stars_repo_path": "NLP programmes in Python/8.Classification/classification.py", "max_stars_repo_name": "AlexandrosPlessias/NLP-Greek-Presentations", "max_stars_repo_head_hexsha": "4ae9d635a777f24bae5238b9f195bd17d0... |
#!/usr/bin/env python3
import time
import argparse
import numpy as np
import gym
import gym_minigrid
from gym_minigrid.wrappers import *
from gym_minigrid.window import Window
def redraw(img):
if not args.agent_view:
img = env.render('rgb_array', tile_size=args.tile_size)
window.show_img(img)
def re... | {"hexsha": "134fd71ba4e529544dbd99ecdb6a649ed2f7d2c5", "size": 2563, "ext": "py", "lang": "Python", "max_stars_repo_path": "manual_control.py", "max_stars_repo_name": "utnnproject/gym-minigrid", "max_stars_repo_head_hexsha": "9e8f9c12964dd36a3c940783d510be525a17e5a8", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import numpy as np
import matplotlib.pyplot as plt
# set the data
x_data = np.linspace(0, 10)
y_data_1 = np.sin(x_data)
y_data_2 = np.cos(x_data)
y_data_3 = [i / 2 for i in y_data_1]
y_data_4 = [j / 2 for j in y_data_2]
# make the plot
ax1 = plt.subplot(2,3,1)
plt.plot(x_data, y_data_1)
plt.setp(ax1.get_xticklabels(... | {"hexsha": "8ad964cd01c819c8f213f80fb9f8c5f411aaaac9", "size": 903, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/numpy/test2.py", "max_stars_repo_name": "lcary/tmp", "max_stars_repo_head_hexsha": "1ea8e06bc25d13f5be6a0ac578d3302ee2134a77", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import math
import numpy as np
import pandas as pd
from scipy.special import expit
import torch
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()
def accuracy(y_pred, y_true, thresh):
outputs = y_pred.unsqueeze(4)
outputs... | {"hexsha": "ade55143f79e11f2c69bad6db4ea407764131768", "size": 7841, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/yolo/yolo_accuracy.py", "max_stars_repo_name": "CMFell/phd_cnn_code", "max_stars_repo_head_hexsha": "cb343bc379f5b06241cead64089a41ae5a6fe167", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
From Coq Require Import ZArith Reals Psatz.
From Coq Require Import Arith.Arith.
Require Import real_lemmas real_model.
From Coquelicot Require Import Coquelicot.
Set Bullet Behavior "Strict Subproofs".
Require Import Interval.Tactic.
Import Coq.Logic.FunctionalExtensionality.
Open Scope R_scope.
(* the function ... | {"author": "VeriNum", "repo": "VerifiedLeapfrog", "sha": "c8d07f86747bd9e44f4cb02f19a691cc895c1279", "save_path": "github-repos/coq/VeriNum-VerifiedLeapfrog", "path": "github-repos/coq/VeriNum-VerifiedLeapfrog/VerifiedLeapfrog-c8d07f86747bd9e44f4cb02f19a691cc895c1279/leapfrog_project/harmonic_oscillator_system.v"} |
[STATEMENT]
lemma adjoint_add:
fixes A B :: "'a::conjugatable_field mat"
assumes "A \<in> carrier_mat n m" "B \<in> carrier_mat n m"
shows "adjoint (A + B) = adjoint A + adjoint B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. adjoint (A + B) = adjoint A + adjoint B
[PROOF STEP]
apply (rule eq_matI)
[PROOF ST... | {"llama_tokens": 540, "file": "QHLProver_Complex_Matrix", "length": 3} |
\<^marker>\<open>creator "Maximilian P. L. Haslbeck"\<close>
theory ERT_Of_IID_Loop_Classic
imports PGCL_With_State IID_Loops
begin
text \<open>This theory is OBSOLETE!
It also tries to prove Theorem 4 from @{cite batzESOP18} and follows the paper
more closely than the prove in Prove_Rule.\<close>
s... | {"author": "maxhaslbeck", "repo": "verERT", "sha": "193188292620a60005e528a78247323eb53084bc", "save_path": "github-repos/isabelle/maxhaslbeck-verERT", "path": "github-repos/isabelle/maxhaslbeck-verERT/verERT-193188292620a60005e528a78247323eb53084bc/ERT_Of_IID_Loop_Classic.thy"} |
using Rubin
using Tests
using Elliptic
using HypergeometricFunctions
using Polylogarithms
using SpecialFunctions
@test integrate((c+d*x)^4*sin(a+b*x), x) == :(-1*b^-1*(c+d*x)^4*cos(a+b*x)+-24*b^-5*d^4*cos(a+b*x)+-24*b^-4*d^3*(c+d*x)*sin(a+b*x)+4*d*b^-2*(c+d*x)^3*sin(a+b*x)+12*b^-3*d^2*(c+d*x)^2*cos(a+b*x))
@test integ... | {"hexsha": "45a5b45220e77c32f15003c89658efabe5f3d48b", "size": 148325, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/4 Trig functions/4.1 Sine/4.1.10 (c+d x)^m (a+b sin)^n.jl", "max_stars_repo_name": "bradeneliason/Rubin.jl", "max_stars_repo_head_hexsha": "4becf75a5b265aced3ecd4e2bc8e7473c68c4840", "max_st... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import multiprocessing as mp
import os
from ... import _init_paths
import cv2
#import detectron2.data.transforms as T
from PIL import Image
from numpy import asarray
import numpy as np
import torch
#from detectron2.checkpoint import ... | {"hexsha": "71eefe8812dec00a8d1d2bb73415aecb0f32156d", "size": 10659, "ext": "py", "lang": "Python", "max_stars_repo_path": "Grad-CAM.pytorch/detection/demo.py", "max_stars_repo_name": "jasonlai777/Faster-R-CNN", "max_stars_repo_head_hexsha": "b5c0c18a9b5faabd4b6ef23346aff85104df7356", "max_stars_repo_licenses": ["MIT"... |
# --------------
# Import Libraries
import os
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
# Code starts here
df=pd.read_csv(path)
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
df=df.replace('NaN', np.nan)
print(df.head())
# Code ends here
# ------... | {"hexsha": "c3fa61ffe21e2f2f25c051882e00220742044aed", "size": 2452, "ext": "py", "lang": "Python", "max_stars_repo_path": "code.py", "max_stars_repo_name": "Jyo172/ga-learner-dsmp-repo-cash-deposit-prediction", "max_stars_repo_head_hexsha": "16b6f2aabc31f131b624a5f06b8c0302f21860a9", "max_stars_repo_licenses": ["MIT"]... |
\documentclass[a4paper]{article}
\input{temp}
\setcounter{section}{-1}
\begin{document}
\title{Representation Theory}
\maketitle
\newpage
\tableofcontents
\newpage
\section{Introduction}
Representaiton theory is the theory of how \emph{groups} act as groups of linear transformations on \emph{vector spaces}.
... | {"hexsha": "346d6ab0ff736aed47610d55880bbb15c9f2f2b6", "size": 98190, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Notes/Representation.tex", "max_stars_repo_name": "raoxiaojia/raoxiaojia.github.io", "max_stars_repo_head_hexsha": "d20c23a64794b500f2e0356fd01017ee31830fa2", "max_stars_repo_licenses": ["CC-BY-3.0... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
import numpy as np
import time
import datetime
import seaborn as sns
import pandas as pd
import os
import gc
import pathlib
import json
import queue
import math
import threading
import re
from random import randrange
import multiprocessing... | {"hexsha": "b983d549a857a79e38c8583e963448aec29ea6ed", "size": 20005, "ext": "py", "lang": "Python", "max_stars_repo_path": "Research/model_utils.py", "max_stars_repo_name": "peterwilli/Endless-AWSW", "max_stars_repo_head_hexsha": "32dd17a01e547b946b9e32b858adc1949a295fd4", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma transrec3_succ [simp]:
"transrec3 a b c (succ i) = b i (transrec3 a b c i)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. transrec3 a b c (ZFC_in_HOL.succ i) = b i (transrec3 a b c i)
[PROOF STEP]
by (simp add: transrec transrec3_def) | {"llama_tokens": 118, "file": "ZFC_in_HOL_ZFC_Cardinals", "length": 1} |
import numpy as np
# from config import INPUT_SIZE
INPUT_SIZE = (448, 448)
_default_anchors_setting = (
dict(layer='p2', stride=32, size=24, scale=[2 ** (1. / 3.), 2 ** (2. / 3.)], aspect_ratio=[0.667, 1, 1.5]),
dict(layer='p3', stride=64, size=48, scale=[2 ** (1. / 3.), 2 ** (2. / 3.)], aspect_ratio=[0.... | {"hexsha": "49f967466d6638a759b9773467c9edd6f3eb4340", "size": 4652, "ext": "py", "lang": "Python", "max_stars_repo_path": "anchors.py", "max_stars_repo_name": "fengjiqiang/related_code", "max_stars_repo_head_hexsha": "72169d94350f4c2e29b0a2b4511c518c8372160d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import sys
sys.path.append("..")
from tqdm import tqdm, trange
import json
import numpy as np
import torch
""" pretrain 데이터셋"""
class PretrainDataSet(torch.utils.data.Dataset):
"""
데이터로더에 사용하기 위한 데이터 셋
is_next: tokens_a와 tokens_b가 연속된 문장인지 여부
tokens: 문장들의 tokens
segment: tokens_a(0)와 tokens_b(1)을 구분하기 위한 값
... | {"hexsha": "c05a7bc30611f6bc164efb13c46de494a48901bf", "size": 2945, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloader/kowiki.py", "max_stars_repo_name": "nawnoes/ReforBERT", "max_stars_repo_head_hexsha": "ae7c8ddf55de2e06080a0e46735ea9609262d8ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from PIL import Image
from .._results import PredictionResult
try:
import tflite_runtime.interpreter as tflite
except ImportError:
# Needs better error text
raise ImportError(
"ERROR: This is a TensorFlow Lite model and requires TensorFlow Lite interpreter to be installed on thi... | {"hexsha": "6b39a4515a2b9ca84fab5d2dc5157ffd81319388", "size": 2097, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lobe/backends/_backend_tflite.py", "max_stars_repo_name": "marlinspike/lobe-python", "max_stars_repo_head_hexsha": "91d1128c34f71526aa0e132f9e2f52e4eb971f56", "max_stars_repo_licenses": ["MIT"... |
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#ifndef SERIAL_COM_TCPSERVER_HPP
#define SERIAL_COM_TCPSERVER_HPP
#include <iostream>
#include <boost/array.hpp>
#include <boost/asio.hpp>
#include <boost/bind.hpp>
#include <boost/thread.hpp>
#include <stdio.h>... | {"hexsha": "21fabb60ebfc71e848472d32050ef722fdbc8b4e", "size": 1246, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "MavLinkCom/src/serial_com/TcpServer.hpp", "max_stars_repo_name": "JaganathSahu/Microsoft-Open-Source-Code", "max_stars_repo_head_hexsha": "bf5cc70395da78f03d7af8592fad466088f9d84e", "max_stars_repo_... |
import nltk
from nltk import word_tokenize
#import speech_recognition as sr_audio
import numpy as np
from textblob import TextBlob
#import transcribe as ts
try:
nltk.data.find('averaged_perceptron_tagger')
except LookupError:
nltk.download('averaged_perceptron_tagger')
def nltk_featurize(file):
#Check if ... | {"hexsha": "e09b1ed35cfaa85fafb526d021c7dbe950c89205", "size": 4860, "ext": "py", "lang": "Python", "max_stars_repo_path": "DigiPsych_API/Feature_Extract_API/nltk_featurize.py", "max_stars_repo_name": "larryzhang95/Voice-Analysis-Pipeline", "max_stars_repo_head_hexsha": "264ac5c70d0baab47b81718ea5b895be30a683e9", "max_... |
"""Simple layer profile plots for group results."""
import os
import numpy as np
import nibabel as nb
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
FIG_DATA = [
"/home/faruk/data2/DATA_MRI_NIFTI/derived/plots/20_depth_vs_T2star/sub-01_depth_vs_T2star.npy",
"/home/faruk/data2/DATA_MRI_N... | {"hexsha": "52ed6d78d6fb0dd6f257b74d2f367e0f9b733f63", "size": 5155, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/05_depth_profiles/03_group_depth_vs_T2star.py", "max_stars_repo_name": "ofgulban/meso-MRI", "max_stars_repo_head_hexsha": "15ef8e19aae6218833a06bf01418d3d83eafd8c7", "max_stars_repo_licens... |
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