text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import streamlit as st
import numpy as np
def display_population_widgets(p, selected_states, selected_counties, nat_data) -> int:
sub_nat = nat_data.loc[nat_data.state.isin(selected_states) & nat_data.county.isin(selected_counties)]
population = int(np.sum(sub_nat.pop_est2019.unique()).item())
st.subheader... | {"hexsha": "ea8c8e200cdb1d187678555604f992bb9515e5ed", "size": 1020, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/penn_chime/population.py", "max_stars_repo_name": "mmastand/chime", "max_stars_repo_head_hexsha": "6012a9b0e921d7d55ad4b8b7432740807cce721d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Pymer4 Lmer Class
=================
Main class to wrap R's lme4 library
"""
from copy import copy
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
from rpy2.rinterface_lib import callbacks
from rpy2.robjects import numpy2ri
import rpy2.rinterface as rinterface
import warnings
import tr... | {"hexsha": "989ede8ecf233a4aba2b8fc7cf6b624d002b46d8", "size": 61938, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymer4/models/Lmer.py", "max_stars_repo_name": "jcheong0428/pymer4", "max_stars_repo_head_hexsha": "7e98fa28f5fdc01e8f786e381179c6b36067ef90", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# Copyright 2019-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" fil... | {"hexsha": "91dd8c96a61baf4a7147b305794958b28fa82b0a", "size": 24753, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/unit_tests/braket/circuits/test_circuit.py", "max_stars_repo_name": "Takuya-Miyazaki/amazon-braket-sdk-python", "max_stars_repo_head_hexsha": "e9c868b3360b1c78d9ecb5222796af1fd2670e29", "max... |
from urllib.parse import MAX_CACHE_SIZE
from transformers.models.bert.modeling_bert import BertForTokenClassification
from transformers.modeling_outputs import SequenceClassifierOutput
from torch.nn import CrossEntropyLoss
from typing import Dict, List
from .abstract_model import (
SpanClassifier,
SpanClassifie... | {"hexsha": "ed7c4e74b2d1f4f6477ed6349fa2686ef5648c38", "size": 15835, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/context.py", "max_stars_repo_name": "fracivilization/entity_typing", "max_stars_repo_head_hexsha": "ca6c327c47639e09adb55e4d84ec4f9867544e28", "max_stars_repo_licenses": ["Apache-2.0"]... |
data Nat : Type where
Z : Nat
S : (1 k : Nat) -> Nat
data Bool : Type where
False : Bool
True : Bool
data Thing : Bool -> Type where
TF : Thing False
TT : Thing True
data Maybe : Type -> Type where
Nothing : {a : Type} -> Maybe a
Just : {a : Type} -> a -> Maybe a
ok : (0 b : ... | {"hexsha": "5403711b7a88987d8c6764e9ea7a73bb719ccf6e", "size": 970, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "idris2/tests/idris2/linear003/Linear.idr", "max_stars_repo_name": "Qqwy/Idris2-Erlang", "max_stars_repo_head_hexsha": "945f9c12d315d73bfda2d441bc5f9f20696b5066", "max_stars_repo_licenses": ["BSD-3-... |
import pandas as pd
import numpy as np
from scipy import stats
import os
from tqdm import tqdm
import pickle as pkl
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
plt.switch_backend('agg')
def stat(seq_length):
print('Seq len info :')
seq_len = np.asarray(seq_length)
... | {"hexsha": "6c8aeb35f3926529db5abbc4de2d289fc0276ad1", "size": 5513, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_preprocess.py", "max_stars_repo_name": "shiningliang/DIMM", "max_stars_repo_head_hexsha": "adc9ff2bea0921cffe91989a1adc95184d81e6a5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
//---------------------------------------------------------------------------//
//!
//! \file Utility_InverseMomentumUnits.hpp
//! \author Alex Robinson
//! \brief The inverse momentum units
//!
//---------------------------------------------------------------------------//
#ifndef UTILITY_INVERSE_MOMENTUM_UNITS_HP... | {"hexsha": "3426f8b12378839e56c19771cb934deec07734de", "size": 873, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "packages/utility/core/src/Utility_InverseMomentumUnits.hpp", "max_stars_repo_name": "bam241/FRENSIE", "max_stars_repo_head_hexsha": "e1760cd792928699c84f2bdce70ff54228e88094", "max_stars_repo_license... |
Require Import Nat Arith.
Inductive Nat : Type := succ : Nat -> Nat | zero : Nat.
Inductive Lst : Type := cons : Nat -> Lst -> Lst | nil : Lst.
Inductive Tree : Type := node : Nat -> Tree -> Tree -> Tree | leaf : Tree.
Inductive Pair : Type := mkpair : Nat -> Nat -> Pair
with ZLst : Type := zcons : Pair -> ZLst ... | {"author": "qsctr", "repo": "coq-quantified-theorems", "sha": "d3456ea0a70121e8de87956b45349aa7b943e37d", "save_path": "github-repos/coq/qsctr-coq-quantified-theorems", "path": "github-repos/coq/qsctr-coq-quantified-theorems/coq-quantified-theorems-d3456ea0a70121e8de87956b45349aa7b943e37d/benchmarks/CLAM/goal21.v"} |
# spectro.py
"""
Classes and definitions related to spectroscopic data.
"""
from __future__ import print_function
import numpy as np
from astropy import wcs
from astropy import units as u
class Line(object):
"""
Class to represent a spectral line.
A Line contains information about the rest wavelength, t... | {"hexsha": "1f53c2c7554239f07f10388d7dfd0c93b719eced", "size": 16657, "ext": "py", "lang": "Python", "max_stars_repo_path": "klpyastro/sciformats/spectro.py", "max_stars_repo_name": "KathleenLabrie/KLpyastro", "max_stars_repo_head_hexsha": "bf29ce13df3e41090fee6ca502167ddd27349aa8", "max_stars_repo_licenses": ["0BSD"],... |
# Third party imports
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from scipy.interpolate import interp1d
from scipy.integrate import odeint
# Local appli... | {"hexsha": "9df11db69847745d740a225a112d661d40a9956b", "size": 7855, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/regressors.py", "max_stars_repo_name": "vasilogi/solid-kinetics", "max_stars_repo_head_hexsha": "c8726fcdeb56e3aed3576dbc3f318e717d020fb2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Multiples in Number Range
Repo: https://github.com/Andrewnetwork/MathematicalProgrammingProblems
## 0.) Definitions
A *range* of natural numbers, positive integers, can be defined by the notation $[x,y]$ where $x$ is the starting number and $y$ is the ending number. Example: $[0,10] = [0,1,2,3,4,5,6,7,8,9,10]$.
... | {"hexsha": "5f34401edf01079130bb6f23704c2d5af7e44596", "size": 5717, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "MathematicalProgrammingProblems/Notebooks/Multiples in Number Range.ipynb", "max_stars_repo_name": "Andrewnetwork/HaskellExercises", "max_stars_repo_head_hexsha": "3caeb255b99bd69a39e... |
C
C $Id: gclrwk.f,v 1.10 2008-07-27 00:20:57 haley Exp $
C
C Copyright (C) 2000
C University Corporation for Atmospheric Research
C All Rights Reserved
C
C The use of this Software is governed by a License Agreem... | {"hexsha": "34645210ca86d8ddf1ab835b90cbe85c8c25bf45", "size": 445, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ncarg2d/src/libncarg_gks/awi/gclrwk.f", "max_stars_repo_name": "tenomoto/ncl", "max_stars_repo_head_hexsha": "a87114a689a1566e9aa03d85bcf6dc7325b47633", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import numpy as np
import math
import ChaoticPSOAlgorithm as PSO
def AR1GARCH11HansenSkewedTOptimize(ret:np.ndarray)->[float]:
log=np.log
pi=math.pi
x=ret[0:(len(ret)-2)]
y=ret[1:(len(ret)-1)]
A=np.vstack([x, np.ones(len(x))]).T
m, c = np.linalg.lstsq(A, y, rcond=None)[0]
def loglik(paramet... | {"hexsha": "476e858ddd4db4129ecdef11bdd58be9d5954e30", "size": 2609, "ext": "py", "lang": "Python", "max_stars_repo_path": "PythonGlobalOptimizationLib/PythonGlobalOptimizationLib/Models/AR1GARCH11HansenSkewedT.py", "max_stars_repo_name": "zhenshaoaixixi0507/PythonGlobalOptimizationLib", "max_stars_repo_head_hexsha": "... |
from core.nlp.response_generator.product.base.base_response_generator import BaseResponseGenerator
import numpy as np
class OYSRepeatResponseGenerator(BaseResponseGenerator):
"""
OYS(On Your Side)
"""
def __call__(self):
responses = self.__create_oys_after_repeat()
self.response_data['... | {"hexsha": "d3c5bf027e49050d889eef9380996009f2ff34d5", "size": 858, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/nlp/response_generator/product/cct/oys_repeat_response_generator.py", "max_stars_repo_name": "hirokig/CBT", "max_stars_repo_head_hexsha": "ac92490d2379f9c331973ca4301c7b10d7774b32", "max_stars... |
# Copyright 2020 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | {"hexsha": "5b2c4d352e98108e9ba2ae77fdbffa1bfec29d27", "size": 1499, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/PR_test/unit_test/backend/test_transpose.py", "max_stars_repo_name": "DwijayDS/fastestimator", "max_stars_repo_head_hexsha": "9b288cb2bd870f971ec4cee09d0b3205e1316a94", "max_stars_repo_licens... |
#! /usr/bin/env python
# Written by Vasaant S/O Krishnan in 2015. Run without arguments for instrunctions."
import ephem
from numpy import *
import sys
import string
inp=sys.argv[0:]
del inp[0]
if len(inp)==0:
print" Script to determine the midpoint between two points"
print" in the sky"
print" Type 'mid... | {"hexsha": "826c04c62595219af79f76870d15e6ebb8edd73f", "size": 2075, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/midPt.py", "max_stars_repo_name": "vasaantk/bin", "max_stars_repo_head_hexsha": "a8c264482ad3e5f78308f53d8af0667b02d6968d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
from keras import backend as K
import numpy as np
import math
from keras.constraints import Constraint
from keras.constraints import MinMaxNorm
from keras.initializers import Constant
from keras.layers import (
MaxPooling2D,
AveragePooling2D,
GlobalMaxPooling2D,
GlobalAveragePooling2D,
Add,
Mult... | {"hexsha": "77dbebb3aafc4f6afa2177074c87fe73dadfb433", "size": 2013, "ext": "py", "lang": "Python", "max_stars_repo_path": "pooling/ow_constraints.py", "max_stars_repo_name": "jiforcen/orderedweightedpooling", "max_stars_repo_head_hexsha": "8cf13f86fcfb132080b5dd56463701f597bf3b60", "max_stars_repo_licenses": ["MIT"], ... |
@testset "Array interface" begin
# Test aliases
@test QuantizedArray(rand(10), k=2, method=:sample) isa QuantizedVector
@test QuantizedArray(rand(2, 10), k=2, method=:sample) isa QuantizedMatrix
# Test outer constructor checks
@test_throws AssertionError QuantizedArray(rand(2, 2, 2), k=2, m=1, met... | {"hexsha": "c3406525b52fa4cf17a8000e458f05f4e17f8a00", "size": 3933, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/interface.jl", "max_stars_repo_name": "UnofficialJuliaMirror/QuantizedArrays.jl-a7db621c-8ce0-11e9-16a1-0f86dc86bd10", "max_stars_repo_head_hexsha": "efcd74e6480f1b54a5dbcd4213a77bc92073df86",... |
#
# This file has been taken and modified from:
# https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py
#
# COPYRIGHT
#
# All contributions by François Chollet:
# Copyright (c) 2015, François Chollet.
# All rights reserved.
#
# Each contributor holds copyright over their respective contrib... | {"hexsha": "c7d42d25e418dfa0b38ad691b46447e3a8b4ee4b", "size": 7269, "ext": "py", "lang": "Python", "max_stars_repo_path": "hyper_param/conv_filters_visualization.py", "max_stars_repo_name": "EnisBerk/hyperopt-keras-sample", "max_stars_repo_head_hexsha": "dc6892f023b83ee3b5b92f2a258676ad6bbc0a94", "max_stars_repo_licen... |
module module_example
! all data can be accessed from out side of this module
implicit none
real, public :: x = 100.
real :: y = 100.
end module module_example
| {"hexsha": "19e0164c40ca786467868bdbea8665bc9dca08ed", "size": 174, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "basics/subroutine/scope/scopingunit_module.f90", "max_stars_repo_name": "ComplicatedPhenomenon/Fortran_Takeoff", "max_stars_repo_head_hexsha": "a13180050367e59a91973af96ab680c2b76097be", "max_sta... |
# -------------------------------------------------------------------
import cv2
import numpy as np
import time
from enum import Enum
# =============================================================================
# Ref. design
# https://github.com/Xilinx/Vitis-AI/blob/v1.1/mpsoc/vitis_ai_dnndk_samples/tf_yolov3_voc_p... | {"hexsha": "0d3b0fd64fe6418b3513e7b92893a88bcf61d71e", "size": 7317, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/dpu_yolov4.py", "max_stars_repo_name": "dramoz/kv260-atrover", "max_stars_repo_head_hexsha": "7b698b5b033dad5dd40c96e2aa61ec7f6a186e0c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma lemma2_6_5_a': assumes t:"trans r" and "(M,N) \<in> mul_eq r" shows "(M -s ds r S, N -s ds r S) \<in> mul_eq r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (M -s r \<down>s S, N -s r \<down>s S) \<in> mul_eq r
[PROOF STEP]
using assms lemma2_6_5_a[OF t] ds_ds_subseteq_ds[OF t]
[PROOF STATE]
proo... | {"llama_tokens": 316, "file": "Decreasing-Diagrams_Decreasing_Diagrams", "length": 2} |
/*
+----------------------------------------------------------------------+
| HipHop for PHP |
+----------------------------------------------------------------------+
| Copyright (c) 2010-present Facebook, Inc. (http://www.facebook.com) |
+---------... | {"hexsha": "283389da2ece22c280c3b766db61a4aed60042b1", "size": 31622, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "hphp/hhbbc/optimize.cpp", "max_stars_repo_name": "alexsn/hhvm", "max_stars_repo_head_hexsha": "6061999778c513d2433c3282902ab1befc4f60b2", "max_stars_repo_licenses": ["PHP-3.01", "Zend-2.0"], "max_s... |
import numpy as np
class Loop(object):
@staticmethod
def train(env, brain_names, models, data, begin_episode, save_frequency, reset_config, max_step, max_episode, sampler_manager, resampling_interval, policy_mode):
assert policy_mode == 'off-policy', "multi-agents algorithms now support off-policy on... | {"hexsha": "72bf3a8f32353177685959502be1e3cd16f9a16b", "size": 7801, "ext": "py", "lang": "Python", "max_stars_repo_path": "ma_loop.py", "max_stars_repo_name": "StepNeverStop/RL-TF1", "max_stars_repo_head_hexsha": "c9e75819504a8db4c587e2aa3e4c9c8845fd9f08", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
# Optimization for Medical Image Segmentation with 2D U-Net on Intel® Xeon Scalable Platform
#### Agenda
1. Background information introduction
2. Intel's optimization technologies on Intel® Xeon Scalable Processors
3. Let's do coding!
### 1. Background Information Introduction
#### 1.1 Brain MRI scan
Magnetic reson... | {"hexsha": "ee5777379bb40b914a9485fe1db40af1e8cf0c6e", "size": 23013, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "2D/04_Inference.ipynb", "max_stars_repo_name": "jingxu10/medical-decathlon", "max_stars_repo_head_hexsha": "711dba6acb1bfb8bac88b4936980bd21b45995bd", "max_stars_repo_licenses": ["Ap... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
##
# @file base.py
# @authors Tiantian Guo
# Kyeong Soo (Joseph) Kim <Kyeongsoo.Kim@xjtlu.edu.cn>
# @date 2019-04-22
#
# @brief Simulate DASH video streaming.
#
# @remarks It is part of Tiantian's master thesis project and
# modified by Kye... | {"hexsha": "e4d7328fdbd74e8a7b8da688c908d725fb3b1656", "size": 6499, "ext": "py", "lang": "Python", "max_stars_repo_path": "base.py", "max_stars_repo_name": "kyeongsoo/dash-simulation", "max_stars_repo_head_hexsha": "ceccfee61d7102146e83b0a2d60d87693c871198", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
[STATEMENT]
lemma red_lcl_add_aux:
"extTA,P,t \<turnstile> \<langle>e, s\<rangle> -ta\<rightarrow> \<langle>e', s'\<rangle> \<Longrightarrow> extTA,P,t \<turnstile> \<langle>e, (hp s, l0 ++ lcl s)\<rangle> -ta\<rightarrow> \<langle>e', (hp s', l0 ++ lcl s')\<rangle>"
and reds_lcl_add_aux:
"extTA,P,t \<turnstile> ... | {"llama_tokens": 19954, "file": "JinjaThreads_J_SmallStep", "length": 33} |
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.load_buffer import load_data_from_npy_chaining,load_data_from_npy_chaining_mult
from rlkit.samplers.data_collector import MdpPathCollector, \
CustomMDPPathCollector
from rlkit.torch.sac.policies import TanhGaussianPolicy, MakeDeterministic
from rlki... | {"hexsha": "38d98342676663bd3e80c762998c82a0c49b0a91", "size": 17889, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/cql_workflow.py", "max_stars_repo_name": "Asap7772/OfflineRlWorkflow", "max_stars_repo_head_hexsha": "d9589bcd752616ddd5a798120227e2bcdb1d8e77", "max_stars_repo_licenses": ["MIT"], "max_... |
#include <boost/random/uniform_int_distribution.hpp>
#include <boost/random/random_device.hpp>
#include <limits>
#include "random.hpp"
#include "fast_hash.hpp"
namespace prologcoin { namespace common {
bool random::for_testing_ = false;
static fast_hash testing_rnd_;
static boost::random::random_device random_;
voi... | {"hexsha": "66f80429889196b088fe4f8b3bf2c8ddece87635", "size": 1948, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/common/random.cpp", "max_stars_repo_name": "datavetaren/prologcoin", "max_stars_repo_head_hexsha": "8583db7d99a8007f634210aefdfb92bf45596fd3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
'''
Created on 4 Sep 2017
@author: ywz
'''
import numpy
class Krylov:
def __init__(self):
pass
def cg(self, Ax, b, cg_iters=10, verbose=False, eps=1e-10):
x = numpy.zeros_like(b)
r = b.copy()
p = b.copy()
r_dot_r = r.dot(r)
for _ in ... | {"hexsha": "cf8fd77c686bd4f71430cd820b725f230506993b", "size": 1147, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter03/krylov.py", "max_stars_repo_name": "jvstinian/Python-Reinforcement-Learning-Projects", "max_stars_repo_head_hexsha": "6c97c68351fc4af426cb5c3583d75aebfabac8aa", "max_stars_repo_licenses"... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
General functions and plotting functions
"""
__author__ = "Josephine Yates"
__email__ = "josephine.yates@yahoo.fr"
import argparse
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import scipy.stats as st... | {"hexsha": "0986ae6ddb19f80a83ef3ade08712e4dbc754e57", "size": 17841, "ext": "py", "lang": "Python", "max_stars_repo_path": "UDN_utils.py", "max_stars_repo_name": "hms-dbmi/UDN-gateway-clusters", "max_stars_repo_head_hexsha": "de6d251762d47c98d4720db1d749cfc8fff30b75", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import matplotlib.pyplot as plt
import numpy as np
def get_dimensions(matrix):
"""
A helper function to get the dimensions of the matrix
Args:
matrix (2D array): A 2D array that is
representing a matrix
Returns:
tuple : A tuple containing the dimensions of
... | {"hexsha": "d7d03f4ed30afd59067e301dec5b7f7282287451", "size": 4492, "ext": "py", "lang": "Python", "max_stars_repo_path": "Communication_Cluster/Ignore/communication_cluster (first try did not work).py", "max_stars_repo_name": "AqeelMohamed/KL_WeightedGraph", "max_stars_repo_head_hexsha": "8592d9ed6220fb3226b4e9c8f1cc... |
!
! (c) 2019 Guide Star Engineering, LLC
! This Software was developed for the US Nuclear Regulatory Commission (US NRC)
! under contract "Multi-Dimensional Physics Implementation into Fuel Analysis under
! Steady-state and Transients (FAST)", contract # NRC-HQ-60-17-C-0007
!
! NEMO - Numerical Engin... | {"hexsha": "d4cbfc1f9d85c9ea8b82e430066385cef7201ca3", "size": 15849, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/FV/src/mesh_optimize/smooth_mesh.f90", "max_stars_repo_name": "sourceryinstitute/MORFEUS-Source", "max_stars_repo_head_hexsha": "38659b1097e89e3dea8f5e7e83acf98bfe6623bd", "max_stars_repo_l... |
[STATEMENT]
lemma (in PolynRg) polyn_expr_tOp_c:"\<lbrakk>pol_coeff S c; pol_coeff S d\<rbrakk> \<Longrightarrow>
\<exists>e. pol_coeff S e \<and> (fst e = fst c + fst d) \<and>
(snd e) (fst e) = (snd c (fst c)) \<cdot>\<^sub>r\<^bsub>S\<^esub> (snd d) (fst d) \<and>
polyn_expr R X (fst e) e =... | {"llama_tokens": 377, "file": "Group-Ring-Module_Algebra5", "length": 1} |
#
# relu paddle model generator
#
import numpy as np
from save_model import saveModel
import sys
def relu(name: str, x):
import paddle as pdpd
pdpd.enable_static()
node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
out = pdpd.nn.functional.relu(node_x)
cpu = pdpd.static.cpu_plac... | {"hexsha": "6952bd27cd8ff6ee11ed4655141f3c0c1a036658", "size": 836, "ext": "py", "lang": "Python", "max_stars_repo_path": "ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_relu.py", "max_stars_repo_name": "monroid/openvino", "max_stars_repo_head_hexsha": "8272b3857ef5be0aaa8abbf7bd0d5d5615dc40b6", "ma... |
# -*- coding: utf-8 -*-
"""
@author: Soufiane Mourragui
2020/06/17
READ PDXE DRUG RESPONSE DATA
"""
import os
import pandas as pd
import numpy as np
from functools import reduce
def read_PDXE_response(PDXE_drug_response_df, PDXE_drug_name, X_target):
# X_target has to be a DataFrame with genes in columns
... | {"hexsha": "b5c8e0a869c2d03046d9416903c61fd67b1fcaf6", "size": 805, "ext": "py", "lang": "Python", "max_stars_repo_path": "read_data/read_PDXE_response.py", "max_stars_repo_name": "fuhrmanj/TRANSACT_manuscript", "max_stars_repo_head_hexsha": "71ca2ec42bdd5d547d4b965aa7f84838bfd5b812", "max_stars_repo_licenses": ["MIT"]... |
(***********************************************************************************
* Copyright (c) 2016-2019 The University of Sheffield, UK
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are me... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/Core_DOM/comm... |
"""
Miscellaneous facial features detection implementation
"""
import cv2
import numpy as np
from enum import Enum
class Eyes(Enum):
LEFT = 1
RIGHT = 2
class FacialFeatures:
eye_key_indicies = [
[
# Left eye
# eye lower contour
33,
7,
... | {"hexsha": "bde232be1efca2f22ed22f1f11299e6947b52db1", "size": 8483, "ext": "py", "lang": "Python", "max_stars_repo_path": "vtuber/facial_features.py", "max_stars_repo_name": "goodspark/VTuber-Python-Unity-bak", "max_stars_repo_head_hexsha": "9ef5ebd34eb831d0355076364025d4dbd203f854", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
def TestData(data):
print(type(data))
print(data)
print(data.strides(32,7))
# In[2]:
def AutoSB( inputs_train,outputs_train):
# firstmodel
from sklearn.neural_network import MLPRegressor
from sklearn.svm import SVR
from sklearn.ensem... | {"hexsha": "f608403e8a86fb3de3bf55dcea5644d786165b16", "size": 4376, "ext": "py", "lang": "Python", "max_stars_repo_path": "Magnetic circuit/AutoML_SM_V1.py", "max_stars_repo_name": "Duchanoy/ASAMS", "max_stars_repo_head_hexsha": "829e9c8c32a4d26b3acdc25de95804aa956e44f3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# 4. faza: Analiza podatkov
tabela3[-dv2,] %>% group_by(Drzava)
# komu v povprečju najvec: tabela5$Stevilo_nastopov in tabela
pod9 <- tabela5[-which(tabela5$Stevilo_nastopov + tabela5$Prvi_nastop < 1993),]
# komu smo dali max točke in kdo je zmagal SLO
df1 <- tabela4 %>% group_by(Leto) %>% filter(Tocke == max(Tock... | {"hexsha": "9fa7f30221bbe0c3ae32aca0ad225d1f2f8e7455", "size": 3078, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "brinapirc/APPR-2020-21", "max_stars_repo_head_hexsha": "19b58cc14ec8fce5c76cdeea956937adc2b957d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
"""This creates Pandas dataframes containing predictions.
"""
__author__ = 'Paul Landes'
from dataclasses import dataclass, field
from typing import Callable, List, Iterable
import logging
import sys
import itertools as it
from pathlib import Path
import numpy as np
import pandas as pd
from zensols.persist import per... | {"hexsha": "d7d5081507b6a74359c0ed8bc548257d3945e5c9", "size": 4416, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/zensols/deeplearn/result/pred.py", "max_stars_repo_name": "plandes/deeplearn", "max_stars_repo_head_hexsha": "925f02200c62a7dc798e474ed94a86e009fd1ebf", "max_stars_repo_licenses": ["MIT... |
#ifndef ILLUMINATE_TEST_WORKER_GROUP_HPP
#define ILLUMINATE_TEST_WORKER_GROUP_HPP
// Includes {{{
#include <boost/optional.hpp>
#include <boost/thread.hpp>
#include <iostream>
#include <vector>
#include "future.hpp"
#include "test_worker.hpp"
// }}}
namespace Illuminate {
//! A group of test workers
/*!
This class ... | {"hexsha": "8039c152985e55b8f910d86b5a69d170f99ff2a7", "size": 1323, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "includes/illuminate/test_worker_group.hpp", "max_stars_repo_name": "gcross/Illuminate", "max_stars_repo_head_hexsha": "862f665ccd4b67411bc332f534e1655585750823", "max_stars_repo_licenses": ["0BSD"],... |
import logging
import os
import gensim.downloader as api
from gensim import matutils
from nltk.tokenize import word_tokenize
import numpy as np
from quasimodo.parameters_reader import ParametersReader
from quasimodo.data_structures.submodule_interface import SubmoduleInterface
from quasimodo.assertion_output.tsv_outp... | {"hexsha": "33511614b0b38ed75ef9ccfe5da157f9571fac0e", "size": 6615, "ext": "py", "lang": "Python", "max_stars_repo_path": "quasimodo/assertion_output/saliency_and_typicality_computation_submodule.py", "max_stars_repo_name": "Aunsiels/CSK", "max_stars_repo_head_hexsha": "c88609bc76d865b4987aaf30ddf1247a2031b1a6", "max_... |
/-
Copyright (c) 2020 Aaron Anderson, Jalex Stark. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Aaron Anderson, Jalex Stark
-/
import linear_algebra.matrix.charpoly.coeff
import linear_algebra.matrix.to_lin
import ring_theory.power_basis
/-!
# The minimal polynomia... | {"author": "nick-kuhn", "repo": "leantools", "sha": "567a98c031fffe3f270b7b8dea48389bc70d7abb", "save_path": "github-repos/lean/nick-kuhn-leantools", "path": "github-repos/lean/nick-kuhn-leantools/leantools-567a98c031fffe3f270b7b8dea48389bc70d7abb/src/linear_algebra/matrix/charpoly/minpoly.lean"} |
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parallel.data_parallel import DataParallel
from util.metrics import batch_pix_accuracy, batch_intersection_union
from . import resnet
up_kwargs = {'mode': 'bilinear', 'align_corners': True}
__a... | {"hexsha": "bd70c5134f3b142e3d6429ef854651ecc5ef605b", "size": 8503, "ext": "py", "lang": "Python", "max_stars_repo_path": "NasUnet/models/base.py", "max_stars_repo_name": "mlvc-lab/Segmentation-NAS", "max_stars_repo_head_hexsha": "a9387a1546dacfa2dc6ee1f70366542a1552e541", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule ONNXRuntime_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("ONNXRuntime")
JLLWrappers.@generate_main_file("ONNXRuntime", UUID("09e6dd1b-8208-5c7e-a336-6e9061773d0b"))
end # module ONNXRuntime_jl... | {"hexsha": "c446a9ba8ccdd32e41396aa3f7e7c88eb786ac65", "size": 322, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ONNXRuntime_jll.jl", "max_stars_repo_name": "JuliaBinaryWrappers/ONNXRuntime_jll.jl", "max_stars_repo_head_hexsha": "38d8b1d39aed84bcf3cee02f064b6aa58c5f0ceb", "max_stars_repo_licenses": ["MIT"]... |
(*
* Copyright 2014, General Dynamics C4 Systems
*
* SPDX-License-Identifier: GPL-2.0-only
*)
theory Interrupt_C
imports CSpace_All Finalise_C
begin
context kernel_m begin
lemma invokeIRQHandler_AckIRQ_ccorres:
"ccorres dc xfdc
invs' (UNIV \<inter> {s. irq_' s = ucast irq}) []
(invokeIRQHandler (Ac... | {"author": "NICTA", "repo": "l4v", "sha": "3c3514fe99082f7b6a6fb8445b8dfc592ff7f02b", "save_path": "github-repos/isabelle/NICTA-l4v", "path": "github-repos/isabelle/NICTA-l4v/l4v-3c3514fe99082f7b6a6fb8445b8dfc592ff7f02b/proof/crefine/ARM_HYP/Interrupt_C.thy"} |
'''
ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points.
'''
import os
import os.path
import json
import numpy as np
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
import provider
def p... | {"hexsha": "78f326e080724ef2735fc52a57639bd42bf65b55", "size": 5582, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelnet_dataset.py", "max_stars_repo_name": "lukovkin/pointnet2", "max_stars_repo_head_hexsha": "04271d7bc4b9a6ab18144be4feb262eba3df2a9c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from distutils.core import setup, Extension
from Cython.Build import cythonize
import numpy as np
import os
os.environ['CC'] = 'gcc'
os.environ['CXX'] = 'g++'
graph_software_dir = '{}/software/'.format(os.path.expanduser('~'))
extensions = [
Extension(
name='multicut',
include_dirs=[np.get_includ... | {"hexsha": "67d448846e02367666433581a972016431446b2d", "size": 864, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithms/setup.py", "max_stars_repo_name": "aplbrain/biologicalgraphs", "max_stars_repo_head_hexsha": "7ef86c8893bcabcb469cf184079456a923f1bab1", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/-
Copyright (c) 2020 Johan Commelin. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johan Commelin
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.data.mv_polynomial.default
import Mathlib.data.fintype.card
import Mathlib.PostPort
univer... | {"author": "AurelienSaue", "repo": "Mathlib4_auto", "sha": "590df64109b08190abe22358fabc3eae000943f2", "save_path": "github-repos/lean/AurelienSaue-Mathlib4_auto", "path": "github-repos/lean/AurelienSaue-Mathlib4_auto/Mathlib4_auto-590df64109b08190abe22358fabc3eae000943f2/Mathlib/ring_theory/polynomial/homogeneous.lean... |
# Copyright (c) 2016 by Mike Jarvis and the other collaborators on GitHub at
# https://github.com/rmjarvis/Piff All rights reserved.
#
# Piff is free software: Redistribution and use in source and binary forms
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redist... | {"hexsha": "be91a471ec040be4aa29269ec1851406503ef756", "size": 37107, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_gsobject_model.py", "max_stars_repo_name": "rgraz/PIFFZTF", "max_stars_repo_head_hexsha": "5f47a7fbdb9040d871f40a5ce7de08740fdfbdb1", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
# coding: utf-8
# In[1]:
from pyaugur.augurlib import AugurOpt, AugurInfer
import numpy as np
import scipy as sp
import scipy.stats as sps
augur_lda = '''(K : Int, D : Int, N : Vec Int, alpha : Vec Real, beta : Vec Real) => {
param theta[d] ~ Dirichlet(alpha)
for d <- 0 until D ;
param phi[k] ~ Dirichlet(... | {"hexsha": "23315204d01d810b504252698acdae932f477a9d", "size": 2393, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/lda.py", "max_stars_repo_name": "rjnw/augurv2", "max_stars_repo_head_hexsha": "0430482297e81288d58a16d43a98ea9d0196d640", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 14,... |
import numpy as np
import os
import datetime
import pygrib as pg
import argparse
import Myhelpers.defaults as defaults
import scipy.sparse as sparse
from solarconversionfunctions import SolarPVConversion, newHayDavies, testSlopeFunction, fullTrackingSlopeFunction
from configobj import ConfigObj
from validate import Va... | {"hexsha": "d8cc87875f210029145d9417eb690b1fa16354d0", "size": 5620, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/Convert_Forecasts/convert_solar_forecast.py", "max_stars_repo_name": "DTU-ELMA/European_Dataset", "max_stars_repo_head_hexsha": "8fb79c61274c95277edc8ee7f60e724e5cc1e0f4", "max_stars_repo_... |
import Base.convert
export MPSQuantumRegister, convert, two_qubit_gate_to_mpo, mpo_to_two_qubit_gate
export print_info, compress!, enaglemment_entropy, execute!
"Implementation type MPS quantum register"
struct MPSQuantumRegister{T} <: QuantumRegister
N::Integer
state::Array{Array{T, 3}, 1}
s_values::Arra... | {"hexsha": "61c4d49d37fde72c31f168889dfa9f1b66630e6c", "size": 6911, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mps_quantum_register.jl", "max_stars_repo_name": "nmoran/OhMyQSIM.jl", "max_stars_repo_head_hexsha": "b99670373b75db0cb975eef1bead3b850ac6f3bf", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features ... | {"hexsha": "81f3e89c68579961ae5a613a09bc10301232359f", "size": 743, "ext": "py", "lang": "Python", "max_stars_repo_path": "janus/tpot/tutorials/tpot_titanic_pipeline.py", "max_stars_repo_name": "josepablocam/janus-public", "max_stars_repo_head_hexsha": "4713092b27d02386bdb408213d8edc0dc5859eec", "max_stars_repo_license... |
import abc
import numpy as np
from tfutils.pyutils import inheritdocstring
class WeightInitializer(abc.ABC):
"""
Args:
input_dim: Positive integer.
output_dim: Positive integer.
"""
def __init__(self, input_dim, output_dim):
self.input_dim = input_dim
self.output_dim =... | {"hexsha": "3e3d4cbc8f26340fb7cbab6d658d113d48fd102e", "size": 3518, "ext": "py", "lang": "Python", "max_stars_repo_path": "related_topics/initializer_aspects.py", "max_stars_repo_name": "shuiruge/generative_models", "max_stars_repo_head_hexsha": "a1765a5ff9aeee8c0325f0c5f40b3537bb82accf", "max_stars_repo_licenses": ["... |
package java.lang;
public class IndexOutOfBoundsException extends RuntimeException {
public IndexOutOfBoundsException() { }
public IndexOutOfBoundsException(String s) { super(s); }
}
| {"hexsha": "8d08cd612201d2aa7db5741e0072a81f5de1582d", "size": 192, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sig-src/java/lang/IndexOutOfBoundsException.jl", "max_stars_repo_name": "HarvardPL/cryptoerase", "max_stars_repo_head_hexsha": "4f0a8282858782894f76abee4e2d21ee7c2e6438", "max_stars_repo_licenses": ... |
# H0: None == BGP
# Compare means with one-way ANOVA
## Bloom
data <- read.csv('../../results_old/filter_types/data_all.csv', sep = ';')
data <- data[which(data$combination=='combination_0' | data$combination=='combination_4'),]
print(data)
kruskal.test(combination ~ time, data = data)
#t.test(data$combination_0, ... | {"hexsha": "c2fd4c85bb9dbb8d115768d5733cb722fabe66e9", "size": 737, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/filter_types_old/none_vs_bgp.r", "max_stars_repo_name": "comunica/Experiments-AMF", "max_stars_repo_head_hexsha": "e133e5994d470f84ab923ca6ef8afa114ed21739", "max_stars_repo_licenses": ["MIT"], ... |
from __future__ import print_function
import argparse
import os.path
import csv
import sys
import scipy.sparse
import numpy as np
import table_utils
import shelve
import pickle
dir_path = os.path.dirname(os.path.realpath(__file__))
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-f'... | {"hexsha": "b3f701e7b61d887c80056818d1831e2f565c0237", "size": 2082, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature-table/build_table.py", "max_stars_repo_name": "fziliott/android-malware-analysis", "max_stars_repo_head_hexsha": "7ca784d0a05d5375cec6de21f8d9446ef9c5cf90", "max_stars_repo_licenses": ["MI... |
import numpy as np
features=[[1,2,3],
[1,4,9],
[1,5,0]]
X = np.array(features)
prices = [5,
13,
5]
y=np.array(prices)
theta = [0]*len(features[0])
theta = np.transpose(np.array(theta))
LEARNING_RATE=0.01
NO_TRAINING_EXAMPLES = len(features)
EPSILON=0.00000000... | {"hexsha": "c2a7fc9fec838d594f5053d530df183fb9a94e5d", "size": 1044, "ext": "py", "lang": "Python", "max_stars_repo_path": "VectorizedLinearRegression.py", "max_stars_repo_name": "islamzedd/MLAlgorithms", "max_stars_repo_head_hexsha": "41ecce7cf24b4f72446f6e27be7d83f7ed1e2825", "max_stars_repo_licenses": ["Apache-2.0"]... |
#include <iostream>
#include <Eigen/Core>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include<opencv2/core/eigen.hpp>
#include <chrono>
#include <sophus/se3.hpp>
#include <g2o/core/base_vertex.h>
#include <g... | {"hexsha": "8468cf3b35643303b40a0a0b2682da16aba31628", "size": 11108, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "my_implementation_1/ch7/pose_estimation_3d2d/pose_estimation_3d2d.cpp", "max_stars_repo_name": "Mingrui-Yu/slambook2", "max_stars_repo_head_hexsha": "d31273192bd9fb5ac618f147105082022c87a005", "max... |
## Future-proofing for Python3+
from __future__ import print_function
## Import dolfin and numpy and time ##
import dolfin as df
import numpy as np
from petsc4py import PETSc
from helper import assign_dof
## Optionally import dolfin_adjoint ##
try:
import dolfin_adjoint as dfa
dolfin_adjoint_found = True
ex... | {"hexsha": "83594c84ffaf5eb00ffeab0656c59c85c82757fc", "size": 8030, "ext": "py", "lang": "Python", "max_stars_repo_path": "pfibs/block_problem.py", "max_stars_repo_name": "iprotasov/pfibs", "max_stars_repo_head_hexsha": "589724369b248971ba76da3f764f4b760b666761", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
###############################################################################
# Author: Wasi Ahmad
# Project: Adversarial Multi-task Learning for Text Classification
# Date Created: 10/18/2017
#
# File Description: This script provides general purpose utility functions that
# are required at different steps in the ex... | {"hexsha": "81f7399fdad820892d563edbfa55b46c61563598", "size": 9581, "ext": "py", "lang": "Python", "max_stars_repo_path": "mtl_sent2vec/shared_private/helper.py", "max_stars_repo_name": "wasiahmad/community_question_answering", "max_stars_repo_head_hexsha": "73d13bc1cdf2ea66d13209c007dcc2767cf2155c", "max_stars_repo_l... |
'''
Authors:
1.the-vishal : Vishal
2.Vikas92155 : Vikas
*/MIT License
Copyright (c) 2020 the-vishal
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limita... | {"hexsha": "d19ddcb26ad841c639c029f2ae5d36d064a1a28f", "size": 8077, "ext": "py", "lang": "Python", "max_stars_repo_path": "backend/main.py", "max_stars_repo_name": "iamxhunt3r/Context-Analyzer", "max_stars_repo_head_hexsha": "1b4f2b63ee7a101f021c7322eadcda0604d0b952", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Mathematical functions for line shapes in S(q,E) modelling.
"""
import numpy as np
###############################################################################
###############################################################################
#######################################################################... | {"hexsha": "879fb3eb1a8b330368f439335755ada85e18f7ef", "size": 5886, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelmiezelb/utils/lineshape_functions.py", "max_stars_repo_name": "LukasBeddrich/modelmiezelb", "max_stars_repo_head_hexsha": "b5be0014c391c9aff26360d175d479df99628dcb", "max_stars_repo_licenses"... |
import numpy as np
from scipy.fftpack import fft, ifft
from sympy import sqrt
from devito import TimeFunction, Function, Inc, Dimension, Eq
def wavefield(model, space_order, save=False, nt=None, fw=True):
name = "u" if fw else "v"
if model.is_tti:
u = TimeFunction(name="%s1" % name, grid=model.grid, ... | {"hexsha": "1fa774e63f34c4a6b8595ae3af299e4a6ba027a0", "size": 3730, "ext": "py", "lang": "Python", "max_stars_repo_path": "louboutin2020SEGtwri/src/TWRI_py/twri_utils.py", "max_stars_repo_name": "pilotlynd/Software.SEG2020", "max_stars_repo_head_hexsha": "fce1213331fcc516d199cea3ffbfecbc1953aef4", "max_stars_repo_lice... |
from __future__ import division # for proper float division
import os
import sys
import math
import time
import types
import functools
import random
import numpy as np
from ddapp import ik
from ddapp import ikconstraints
from ddapp import ikconstraintencoder
import drc as lcmdrc
import json
from ddapp.utime import get... | {"hexsha": "98d5e35f4347a14452341b65ada4bb1d765d8a23", "size": 5360, "ext": "py", "lang": "Python", "max_stars_repo_path": "externals/director/src/python/ddapp/plannerPublisher.py", "max_stars_repo_name": "ericmanzi/double_pendulum_lqr", "max_stars_repo_head_hexsha": "76bba3091295abb7d412c4a3156258918f280c96", "max_sta... |
module MYASM1
using Modia
setLogMerge(false)
include("../src/ModelMechanic.jl")
include("../src/ModelEPSphasor.jl")
##################################
VarVolts = Model( # Voltage dip
Tstart = 0.0,
dT = 0.15,
Vmin = 0.0,
y = output,
equations = :[
y = if after(Tstart) && !after(Ts... | {"hexsha": "0fca8321291a150e8c638ad7b2ffd06e614f9290", "size": 1648, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/myASM1.jl", "max_stars_repo_name": "johhell/EPSphasor", "max_stars_repo_head_hexsha": "ef2ae21c976657f09e51347d88b02d9bfdfee3a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 20 17:48:08 2020
@author: suelto
Modified from Jason Brownlee blog:
https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/
This code seeks to implement a random search for hyperparameter estimation.
... | {"hexsha": "840e00bff3c573743db783c81507f5c990a9395c", "size": 5193, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/gridsearchtest.py", "max_stars_repo_name": "bloose/bias_correction_by_ML", "max_stars_repo_head_hexsha": "c0ae25f2bd656ab180adac0e228a3d4ffdec84cd", "max_stars_repo_licenses": ["MIT"], "... |
import os
import tensorflow as tf
import numpy as np
import random
import math
from matplotlib import pyplot as plt
from preprocessing import get_data
from tensorflow.keras import Sequential
from tensorflow.math import exp, sqrt, square
from tensorflow.keras.applications import ResNet50V2
from tensorflow.keras import S... | {"hexsha": "5c4ec4aaa5389988ca44ed7143efbe4f9f522b8f", "size": 5959, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_learning/code/model.py", "max_stars_repo_name": "Naveen-and-Taishi/recycleAtBrown", "max_stars_repo_head_hexsha": "397c1fdd70ac9d2dd977ae5de9ff36808d6af63d", "max_stars_repo_licenses": ["MIT"... |
import cv2
import numpy as np
import transformations as tf
import math as m
import time
import argparse
import threading
from apscheduler.schedulers.background import BackgroundScheduler
from dronekit import connect, VehicleMode, LocationGlobalRelative, Command, LocationGlobal
from pymavlink import mavutil
... | {"hexsha": "25cca6124d479bfd570221187843af982411d111", "size": 7361, "ext": "py", "lang": "Python", "max_stars_repo_path": "Non_T265_Land.py", "max_stars_repo_name": "cloudtenno/autonomous_landing", "max_stars_repo_head_hexsha": "be91679a40f3ee74c9691925fd2b42f723ebf920", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import piexif
import piexif.helper
import PIL
from PIL import Image,ImageChops
from PIL.ExifTags import TAGS
import os
from DateTime import DateTime
import datetime
import pandas as pd
import numpy
import math
import csv
import sys
#sys.path.insert(0,'C:\F_archive\easystore\Python_Programs')
sys.path.in... | {"hexsha": "3dbf9f3bafcdb5f1e9fa0b10c4540749ba846d9e", "size": 11472, "ext": "py", "lang": "Python", "max_stars_repo_path": "reprocess_and save_images/logger_only.py", "max_stars_repo_name": "jcjumley/FlightPathBuilder", "max_stars_repo_head_hexsha": "487a358d2b213ccc4aa8db47b5b4ad0481d6c48a", "max_stars_repo_licenses"... |
// Copyright (c) 2007-2013 Hartmut Kaiser
// Copyright (c) 2011 Bryce Lelbach
// Copyright (c) 2008-2009 Chirag Dekate, Anshul Tandon
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#if !defined(HPX_TH... | {"hexsha": "fd60b673a317bc8baa20de8aab6b33a1005f25e7", "size": 46232, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "hpx/runtime/threads/thread_helpers.hpp", "max_stars_repo_name": "Titzi90/hpx", "max_stars_repo_head_hexsha": "150fb0de1cfe40c26a722918097199147957b45c", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
import numpy as np
from cv2 import VideoCapture,cvtColor,COLOR_BGR2GRAY,imread
class VideoSource:
def __init__(self,name):
self.name = name
self.size = (0,0)
pass
def getFrame(self):
return []
def getSize(self):
return self.size
def g... | {"hexsha": "f143b8aa3bb3ce7420afe6a957bc7465173cd315", "size": 1469, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/VideoSources.py", "max_stars_repo_name": "wgaylord/HAMVideo", "max_stars_repo_head_hexsha": "b6d3494d67d52d8f3002271195505ab1ff40bdbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import sys
import os
import matplotlib as mpl
sys.path.append(os.path.abspath('./plot/'))
from option import *
import matplotlib.pyplot as plot
import matplotlib.ticker as ticker
import numpy as np
import csv
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() +... | {"hexsha": "ec6db0bbe36499a9400d4501dc34797555fdbdd1", "size": 4357, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot/microbench/sample_size_sweep/cpr_url.py", "max_stars_repo_name": "XinYao1994/HOPE", "max_stars_repo_head_hexsha": "99b41b457b67d3e5d6dd182f8aa2ce4ea66e4a68", "max_stars_repo_licenses": ["Apac... |
# TODO Rework this whole file for new dataloader format
import os
import glob
import torch
import imageio
import numpy as np
import pickle as pkl
from .process_spin import process_spin_data
from .load_surreal import dilate_masks
from collections import OrderedDict
def read_3dhp_spin_data(data_path, subject='S1', ext_s... | {"hexsha": "44879a7f4f4edf0c36c40c54d7dcd02a4eb34833", "size": 8216, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/load_3dhp.py", "max_stars_repo_name": "liruilong940607/A-NeRF", "max_stars_repo_head_hexsha": "19cb6c4fd389266214ac0d7215a44011cb1bebf5", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#tests a post processed version
#!/usr/bin/env python
import numpy as np
from matplotlib import pyplot as plt
import pyefd
import Grasping
import cv2 as cv
import rospy
from std_msgs.msg import String
def find_current_grasp():
# find the contour of the image.
# img = cv.imread('test5.png', 0)
img = cv.im... | {"hexsha": "d51939c061667f076d677a0f175baad5335e5410", "size": 2600, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_pp.py", "max_stars_repo_name": "jpchiodini/Grasp-Planning", "max_stars_repo_head_hexsha": "e31234244b8f934743605ebea59d9d98a258957e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*- coding: utf-8 -*-
"""
@author: salimt
"""
# Part B: Problem 3
# Bookmark this page
# Part B: Problem 3: Implementing a Simulation With Drugs
# 10.0/10.0 points (graded)
# In this problem, we consider the effects of both administering drugs to the patient and the ability of virus particle offsprings to
# inherit ... | {"hexsha": "0effa064b9cdcacbd04f0cfc25d88187e5b73275", "size": 6577, "ext": "py", "lang": "Python", "max_stars_repo_path": "MITx-6.00.2x/pset3-virus-patient-simulation/Problem-3-resistant-virus-with-drugs.py", "max_stars_repo_name": "FTiniNadhirah/Coursera-courses-answers", "max_stars_repo_head_hexsha": "d59311917b740a... |
import numpy as np
import cv2 as cv
import pandas as pd
import os
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
import warnings
def load_dataframe():
'''
Carga un dataframe Pandas con las imagenes par... | {"hexsha": "365087ee26f3df48c4b7518d3bc06c4331b0b946", "size": 2163, "ext": "py", "lang": "Python", "max_stars_repo_path": "functions.py", "max_stars_repo_name": "kbueso/Python", "max_stars_repo_head_hexsha": "a18a23bbf6ba3f214c2ed751a20348fe415c6dbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
using Accumulo
using Base.Test
const SEP = "\n" * "="^20 * "\n"
print_header(message) = println(SEP, message, SEP)
function print_table_config(session, tbl)
cfg = table_config(session, tbl)
println("Configuration of $tbl:")
for (n,v) in cfg
println("\t$n => $v")
end
end
function print_table_i... | {"hexsha": "6113278507b11ac3a164cc861370ecc56c8fa856", "size": 6635, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_accumulo.jl", "max_stars_repo_name": "JuliaPackageMirrors/Accumulo.jl", "max_stars_repo_head_hexsha": "dc0d083bd2f89b470933221b7162155f9ff9577f", "max_stars_repo_licenses": ["MIT"], "max_... |
# Velocity Calculation for the Robot based on MDH frames
# Introduction
# Berechnung der Geschwindigkeit von Koordinatensystemen und Schwerpunkten
#
# Dateiname:
# robot -> Berechnung für allgemeinen Roboter
# tree -> Berechnung für eine beliebige Baumstruktur (ohne Schleifen)
# floatb_twist -> floating base wird dur... | {"hexsha": "61b76bd6734d36a6b48a8745f86411f748aa4710", "size": 7261, "ext": "mpl", "lang": "Maple", "max_stars_repo_path": "robot_codegen_kinematics/robot_tree_floatb_rotmat_velocity_linkframe.mpl", "max_stars_repo_name": "SchapplM/robsynth-modelgen", "max_stars_repo_head_hexsha": "33b345ae0dd6ec4aa15499ab3d43edbbded0b... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import os
import sys
LOG_LEVEL_INDEX = sys.argv.index('--log_level') + 1 if '--log_level' in sys.argv else 0
DESIRED_LOG_LEVEL = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else '3'
os.e... | {"hexsha": "7c591567ecae58cd202dd658e3c125c7601e0422", "size": 39868, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/deepspeech_training/export_gradients.py", "max_stars_repo_name": "googleinterns/deepspeech-reconstruction", "max_stars_repo_head_hexsha": "72f28d1e9064d221b3421c302a8725a8c71859ee", "max_star... |
import numpy as np
from msdsl import *
r, c = 1e3, 1e-9
m = MixedSignalModel('rc')
x = m.add_analog_input('x')
dt = m.add_analog_input('dt')
y = m.add_analog_output('y')
func = lambda dt: np.exp(-dt/(r*c))
f = m.make_function(func,
domain=[0, 10*r*c], numel=512, order=1)
a = m.set_from_sync_func('a', f, dt)
x_prev ... | {"hexsha": "23b1ab54a4086183b31bfb8475dfb02032badfd9", "size": 455, "ext": "py", "lang": "Python", "max_stars_repo_path": "random/func.py", "max_stars_repo_name": "sgherbst/msdsl", "max_stars_repo_head_hexsha": "e38d5ecdb88b3574bda62f22a4f91ce3e4173d12", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
# CODE NAME HERE
# CODE DESCRIPTION HERE
Created on 2020-05-21
@author: cook
"""
import numpy as np
# https://en.wikipedia.org/wiki/Adler-32
# Alder 32 algorithm
# try with sample string:
#
# string = 'Wikipedia'
# =================================================... | {"hexsha": "6e4fd4a101d5bae60563f82a281ac2825cbe00ab", "size": 1412, "ext": "py", "lang": "Python", "max_stars_repo_path": "updates_to_drs/ea_alder32_code.py", "max_stars_repo_name": "njcuk9999/apero-utils", "max_stars_repo_head_hexsha": "f77de4c9123874e5bb6ed6bd03a7de3b27057402", "max_stars_repo_licenses": ["MIT"], "m... |
function imview(colortype::Type{<:ColorTypes.Colorant}, imgvalues...)
return PermutedDimsArray(
ImageCore.colorview(
colortype,
(
ImageCore.normedview(
ImageCore.Normed{eltype(img),8 * sizeof(eltype(img))},
img,
... | {"hexsha": "0b7089122ea67993550a81ff531c784e6383791c", "size": 6557, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/raster/images.jl", "max_stars_repo_name": "mattwigway/ArchGDAL.jl", "max_stars_repo_head_hexsha": "3f63e18545286fadbfbf386a06a148118808e8a0", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from collections import OrderedDict
from torchvision import models
import zipfile
import io
import tqdm
def uncertain_logits_to_probs(logits):
"""Convert explicit uncertainty modeling logits to probabiliti... | {"hexsha": "b25dcfc994bbee9a1e5c893d100d4084caab445a", "size": 8674, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchxrayvision/baseline_models/chexpert/model.py", "max_stars_repo_name": "KiLJ4EdeN/torchxrayvision", "max_stars_repo_head_hexsha": "18985291b217d51bd7d46c8a0dc069a78a82755e", "max_stars_repo_li... |
#ifndef TIKPP_DETAIL_TYPE_TRAITS_STREAM_HPP
#define TIKPP_DETAIL_TYPE_TRAITS_STREAM_HPP
#include "tikpp/detail/type_traits/macros.hpp"
#include <boost/asio/buffer.hpp>
#include <boost/system/error_code.hpp>
#include <functional>
#include <type_traits>
namespace tikpp::detail::type_traits {
HAS_MEMBER_FUNCTION(asyn... | {"hexsha": "b8f763e2958e9f5a553801a79764e4c6ce0089d4", "size": 1263, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/tikpp/detail/type_traits/stream.hpp", "max_stars_repo_name": "aymanalqadhi/tikpp", "max_stars_repo_head_hexsha": "8e94abdc4ac8c85dd893780ad4256cdd6690a758", "max_stars_repo_licenses": ["Apac... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Consistency check for location at sea QC test.
"""
from numpy import ma
from cotede.qctests.location_at_sea import (
LocationAtSea,
location_at_sea,
get_bathymetry,
)
from data import DummyData
def test_bathymetry_point():
"""Check the elevation of ... | {"hexsha": "81dd8a969e17be543578018f3092d8d533a7b54a", "size": 4597, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/qctests/test_qc_location_at_sea.py", "max_stars_repo_name": "jessicaaustin/CoTeDe", "max_stars_repo_head_hexsha": "0ca2a1c71de980d91262fd36fd5d8ab8cc09f019", "max_stars_repo_licenses": ["BSD... |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# ===========
# Libraries
# ===========
import os
import sys
import time
import warnings
import imageio
import matplotlib.pyplot as plt
import numpy as np
from skimage import exposure, img_as_uint
# Custom Libraries
from modules import metrics
from modules.args import args
... | {"hexsha": "7d02da9f24305969817a234467ddc22679516553", "size": 5018, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/evaluate_depth_densification_by_close_operation.py", "max_stars_repo_name": "nicolasrosa/Sparse-to-Continuous", "max_stars_repo_head_hexsha": "8664de17d6b6c6cc39bf8fcebfcb829249367f2f",... |
HANSARD REVISE * NUMERO 22
Le mardi 28 octobre 1997
Presentation et premiere lecture>
LOI DE MISE EN OEUVRE DE L'ACCORD CANADA-YUKON SUR LE PETROLE
LOI SUR LA GESTION DES RESSOURCES DE LA VALLEE DU MACKENZIE
L'hon. Ethel Blondin-Andrew
LES SERVICES D'INCENDIE ET D'URGENCE DE MISSISSAUGA
LA CAMETOID ADVANCED TECH... | {"hexsha": "4921655c48ccaa11eb4b597df4c3372e6c4251e6", "size": 66318, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "data/Hansard/Training/hansard.36.1.house.debates.022.f", "max_stars_repo_name": "j1ai/Canadian_Hansards_Neural_Machine_Translation", "max_stars_repo_head_hexsha": "554666a89090fc1b1d1fb83601a2e9d... |
import pickle
import random
import time
import sys
import numpy as np
import tensorflow as tf
import process_text
from scipy import spatial
import argparse
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('summaries_dir', 'data\dssm-400-120-relu', 'Summaries directory')
flags.DEFINE_float('learning_rate',... | {"hexsha": "69d391cc243f738d17a04ba8943ec383b0b561ab", "size": 10332, "ext": "py", "lang": "Python", "max_stars_repo_path": "DLScripts/samples/dssm.py", "max_stars_repo_name": "StanleyLeiSun/PlayGround", "max_stars_repo_head_hexsha": "e8774ef41043e88cc64fc1eacbf0edd99a40ba35", "max_stars_repo_licenses": ["Apache-2.0"],... |
import dash
import dash_bootstrap_components as dbc
import dash_html_components as html
import json
from pathlib import Path
from json_schema_to_dash_forms import SchemaFormContainer
from dash.dependencies import Input, Output, State
import numpy as np
import json
# Font Awesome and bootstrap CSS required
FONT_AWESOM... | {"hexsha": "37a8ed068e71e77a7b2b9087f0d2d12ad25e816b", "size": 3199, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/standalone_example.py", "max_stars_repo_name": "catalystneuro/json-schema-to-dash-forms", "max_stars_repo_head_hexsha": "a6e83a02f3ac2dd0ec2c7d09ec4327f3c6512ee4", "max_stars_repo_license... |
using SimpleCG
plane_x = Plane(Vector3(0, 1, 0), 0)
plane_y = Plane(Vector3(0, 0, 1), -50)
plane_z = Plane(Vector3(1, 0, 0), -20)
sphere = Sphere(Vector3(0, 10, -10), 10)
# light = DirectionalLight(White, Vector3(-1.75, -2, -1.5))
# light = PointLight(White * 2000, Vector3(30, 40, 20))
light = SpotLight(White * 2000... | {"hexsha": "ec5c489c7c3a639076f551592e597edd88330459", "size": 657, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/render_light.jl", "max_stars_repo_name": "sunoru/SimpleeCG.jl", "max_stars_repo_head_hexsha": "07a1fb747973e6b09ec71dcceba66e1f93bcbc7c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 8 11:46:10 2019
@author: roshanprakash
"""
import pandas as pd
import numpy as np
import tensorflow as tf
tf.set_random_seed(35)
np.random.seed(3)
import networkx as nx
from GraphUtils import *
from Loss import *
class Component:
""" A functi... | {"hexsha": "c0392d77fdf413696e4918e36d50401be33f8021", "size": 9720, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/CausalGraphicalModel.py", "max_stars_repo_name": "roshan19041/Causal-Discovery", "max_stars_repo_head_hexsha": "900cfc94d9fc3ff3d75366b00bda3acd044ed638", "max_stars_repo_licenses": ["MIT"], "... |
import matplotlib.pyplot as plt
import pandas as pd
from numpy import arange, array
import os
import logging
logging.basicConfig()
logger = logging.getLogger('PlotTimeCost')
logger.setLevel('INFO')
class PlotTimeCostBar:
def __init__(self, data, path, show=False):
self.data = data
self.path = pa... | {"hexsha": "f706739fa5fbb45a327cc6482a6f9e131f810e2c", "size": 1404, "ext": "py", "lang": "Python", "max_stars_repo_path": "visualizer/plot_mf_param_opt/plot_time_cost_bar.py", "max_stars_repo_name": "buctlab/NIO", "max_stars_repo_head_hexsha": "094e688dd1cd3def7f31cd16ff927d4324651422", "max_stars_repo_licenses": ["Ap... |
from pathlib import Path
from typing import Dict, Union
import numpy as np
from wai_data_tools.io import load_frames, save_frames
import matplotlib.pyplot as plt
def temporal_encoding(frame_dicts: Dict[int, Dict[str, Union[bool, np.ndarray]]], window_size, rgb=False):
"""
Performs temporal normalizations an... | {"hexsha": "edf7b5b818007b2b511014c849236ac406a6af4b", "size": 5420, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/wai_data_tools/temporal_encoding.py", "max_stars_repo_name": "davidaderup/wai_data_tools", "max_stars_repo_head_hexsha": "3057c2be43e05cc88c086c45e0d58eece27b5af0", "max_stars_repo_licenses": ... |
"""Operations to support categorical data."""
# Copyright 2019 CSIRO (Data61)
#
# 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 req... | {"hexsha": "0c06f753c512a2d8a61d38d98e7b0b22e8ac6e36", "size": 6330, "ext": "py", "lang": "Python", "max_stars_repo_path": "landshark/category.py", "max_stars_repo_name": "basaks/landshark", "max_stars_repo_head_hexsha": "87ec1fada74addd58f37bdaf3b1adbc10b1544b2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
#include <boost/algorithm/string.hpp>
#include <boost/archive/xml_iarchive.hpp>
#include <boost/archive/xml_oarchive.hpp>
#include "MeshingParametersDataIO.h"
#include "MeshingParametersBoostIO.h"
#include <MeshingParametersData.h>
#include <IO/MeshingKernelIOMimeTypes.h>
#include <IO/IOUtilDataSerializer.h>
REGIST... | {"hexsha": "5059f6f13c40a2a5e3e103903540a0e386f1cb75", "size": 2099, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Modules/CGALVMTKMeshingKernel/IO/MeshingParametersDataIO.cpp", "max_stars_repo_name": "carthurs/CRIMSONGUI", "max_stars_repo_head_hexsha": "1464df9c4d04cf3ba131ca90b91988a06845c68e", "max_stars_repo... |
# -*- coding:utf-8 -*-
# Created Time: 2018/05/10 17:22:38
# Author: Taihong Xiao <xiaotaihong@126.com>
import os
import scipy.ndimage as nd
import scipy.io as sio
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import torch
from torch.utils.data import Dataset, DataLoader
from tor... | {"hexsha": "b7c912072faa432bfc580636c866eb82dd8a52ee", "size": 3462, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset.py", "max_stars_repo_name": "Prinsphield/3D-GAN-pytorch", "max_stars_repo_head_hexsha": "06b4b34e499a5c5187b6a67a8c8dfc35b2e1ce62", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
\section{\module{types} ---
Names for all built-in types}
\declaremodule{standard}{types}
\modulesynopsis{Names for all built-in types.}
This module defines names for all object types that are used by the
standard Python interpreter, but not for the types defined by various
extension modules. It is safe to... | {"hexsha": "8ade4a6be956aa18e0fe0a49e9e2b22fbde2cb7e", "size": 3749, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/lib/libtypes.tex", "max_stars_repo_name": "marcosptf/cpython-2.0.1", "max_stars_repo_head_hexsha": "73c739a764e8b1dc84640e73b880bc66e1916bca", "max_stars_repo_licenses": ["PSF-2.0"], "max_stars_... |
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