text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
class GradientDescent:
"""
*****************************************************
Batch Gradient Descent Implementation In Python 2.7
*****************************************************
Dependency: numpy
GradientDescent Class
=====================================
... | {"hexsha": "e16537e67ce347e9e4bf0eb9291a7c7200729faa", "size": 3354, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithms/GradientDescent/BatchGradientDescent.py", "max_stars_repo_name": "powerlim2/python", "max_stars_repo_head_hexsha": "2aa66826de3c82ebdf0dd7803cd6b076e9c4d448", "max_stars_repo_licenses":... |
from tabula import read_pdf
from pikepdf import Pdf
import pandas as pd
import numpy as np
import re
import logging
def get_raw_df(filename, num_pages, config):
dfs = []
_pandas_options = {"dtype": str}
header = True
if config["layout"].get("pandas_options"):
_pandas_options.update(config["lay... | {"hexsha": "82a540a9c3a2b6b80e346da76ddd330d75cbcaf4", "size": 5959, "ext": "py", "lang": "Python", "max_stars_repo_path": "pdf_statement_reader/parse.py", "max_stars_repo_name": "flywire/pdf_statement_reader", "max_stars_repo_head_hexsha": "f0e91a4b9a48ea4ef043000bcffba2f5ceff4a52", "max_stars_repo_licenses": ["MIT"],... |
Neural network for importance sampling supplemented variational inference.
## Motivation
As the visualization of shallow neural network on MNIST dataset shows, the fitting of the PDF of the posterior via variational inference needs further finization. This calls for adding more (trainable) degree of freedom to the in... | {"hexsha": "a3ad3e42c171789169b14bbc407c291c898e6338", "size": 286088, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "nn4infer/Importance Sampling.ipynb", "max_stars_repo_name": "shuiruge/nn4de", "max_stars_repo_head_hexsha": "796d9b58bcef018233f1f9a0cb76d0a84000d611", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""Convolutional MoE layers. The code here is based on the implementation of the standard convolutional layers in Keras.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras import activations, initializers, regularizers, constraints
from... | {"hexsha": "711bc16a1bf552f33dbdd96c9ec696fe9886e55f", "size": 22073, "ext": "py", "lang": "Python", "max_stars_repo_path": "machinelearning-benchmark/dl/mixture-of-experts/ConvolutionalMoE.py", "max_stars_repo_name": "YyongXin/tf-mets", "max_stars_repo_head_hexsha": "dacd9398170f5135feb7135b635d4cc3f6869369", "max_sta... |
import matplotlib.pyplot as plt
import numpy as np
nus_lpf,mu_lpf=np.load("clpf.npz",allow_pickle=True)["arr_0"]
nus_modit,mu_modit=np.load("cmodit4500.npz",allow_pickle=True)["arr_0"]
fig=plt.figure(figsize=(8,4))
plt.plot(nus_modit,mu_modit,label="MODIT",color="C1")
plt.plot(nus_lpf,mu_lpf,label="DIRECT",ls="dashe... | {"hexsha": "280ad8f066a419b0b33a4f222f7a1fe98bd0ae34", "size": 453, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/LUH16A/COMP/plotcomp.py", "max_stars_repo_name": "dcmvdbekerom/exojax", "max_stars_repo_head_hexsha": "9b9305f8e383c73bdb97c1cfb0e276ddafcd75de", "max_stars_repo_licenses": ["MIT"], "max_s... |
import comet_ml
import torch
import os
import argparse
from comet_ml.api import API, APIExperiment
import yaml
import torch.nn as NN
import numpy as np
import torch.utils.data as data_utils
from tqdm import tqdm as tqdm
import deepracing_models.nn_models.StateEstimationModels as SEM
import io
parser = argparse.Argumen... | {"hexsha": "889ac4f7a22c56525e4e6d8ab57b201e9d55b017", "size": 3669, "ext": "py", "lang": "Python", "max_stars_repo_path": "DCNN-Pytorch/get_comet_experiment_eap.py", "max_stars_repo_name": "linklab-uva/deepracing", "max_stars_repo_head_hexsha": "fc25c47658277df029e7399d295d97a75fe85216", "max_stars_repo_licenses": ["A... |
[STATEMENT]
lemma periodic_orbit_period:
assumes "periodic_orbit x"
shows "period x > 0" "flow0 x (period x) = x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 < period x &&& flow0 x (period x) = x
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. 0 < period x
2. flow0 x (period x) = x
... | {"llama_tokens": 1348, "file": "Poincare_Bendixson_Periodic_Orbit", "length": 15} |
(************************************************************************************)
(** *)
(** The SQLEngines Library *)
(** ... | {"author": "PrincetonUniversity", "repo": "DeepSpecDB", "sha": "a67d933b4288498bd04c70748b7fa28f676983c3", "save_path": "github-repos/coq/PrincetonUniversity-DeepSpecDB", "path": "github-repos/coq/PrincetonUniversity-DeepSpecDB/DeepSpecDB-a67d933b4288498bd04c70748b7fa28f676983c3/verif/db/plans/Group.v"} |
import codecs
from numpy import *
from time import sleep
def loadDataSet(fileName):
dataMat = [];
labelMat = []
fr = codecs.open(fileName)
for line in fr.readlines():
lineArr = line.strip().split('\t')
dataMat.append([float(lineArr[0]), float(lineArr[1])])
labelMat.append(float... | {"hexsha": "530bd4051d98df903d2d225c396a9cabe5582396", "size": 12754, "ext": "py", "lang": "Python", "max_stars_repo_path": "classes/svmkernel.py", "max_stars_repo_name": "IvarsSaudinis/monty-python-and-the-holy-grail", "max_stars_repo_head_hexsha": "ff17dee0f341a756b6a972b2c71755381a1eae5f", "max_stars_repo_licenses":... |
#! /usr/bin/env python
"""
InstrumentData Class -- defines data format, wavelength info, mask geometry
Instruments/masks supported:
NIRISS AMI
GPI, VISIR, NIRC2 removed - too much changed for the JWST NIRISS class
"""
# Standard Imports
import numpy as np
from astropy.io import fits
import os, sys, time
import copy
... | {"hexsha": "3ab46632f2488904ed4e0bf9faed6b2a0899ecf6", "size": 29326, "ext": "py", "lang": "Python", "max_stars_repo_path": "nrm_analysis/InstrumentData.py", "max_stars_repo_name": "vandalt/ImPlaneIA", "max_stars_repo_head_hexsha": "72b22e487ef45a8a665e4a6a88a91e99e382fdd0", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
//------------------------------------------------------------------------------
/*
Copyright (c) 2012, 2013 Ripple Labs Inc.
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this ... | {"hexsha": "884666ca37bd8ad6a91c5151fa1ad9687b895795", "size": 5362, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ripple/nodestore/backend/MemoryFactory.cpp", "max_stars_repo_name": "ripplealpha/ripple-alpha-core", "max_stars_repo_head_hexsha": "509118209407d46ce29d2889b982b8999fb1eeaa", "max_stars_repo_lic... |
{-# OPTIONS --without-K #-}
open import HoTT
open import cohomology.Theory
{- Cohomology groups of the n-torus (S¹)ⁿ.
- We have Ĉᵏ(Tⁿ) == C⁰(S⁰)^(n choose' k) where _choose'_ defined as below.
- This argument could give Cᵏ((Sᵐ)ⁿ) with a little more work. -}
module cohomology.Torus {i} (OT : OrdinaryTheory i) wher... | {"hexsha": "92ad38abee8a5cd1dc9a1c798017cd838cef2532", "size": 2850, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "cohomology/Torus.agda", "max_stars_repo_name": "danbornside/HoTT-Agda", "max_stars_repo_head_hexsha": "1695a7f3dc60177457855ae846bbd86fcd96983e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
'''
Created on 29 Jan 2022
@author: ucacsjj
'''
import copy
import numpy as np
# This class implements the policy evaluation algorithm.
from .robot_states_and_actions import *
class PolicyEvaluator:
def __init__(self, environment):
# The environment the system works with
self... | {"hexsha": "70299ebdf6a637c8f52da2bef2dc65a195329b7b", "size": 3058, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lab_Week_05_-_Value_Functions,_Policies_and_Policy_Iteration/Solutions/recycling_robot/policy_evaluator.py", "max_stars_repo_name": "annasu1225/COMP0037-21_22", "max_stars_repo_head_hexsha": "e98e... |
"""
# -*- coding: utf-8 -*-
-----------------------------------------------------------------------------------
# Author: Nguyen Mau Dung
# DoC: 2020.08.10
# email: nguyenmaudung93.kstn@gmail.com
-----------------------------------------------------------------------------------
# Description: Testing script
"""
impor... | {"hexsha": "2d6c3b2a5202bf1b5e20ec4cf4d8c04e42dcfe12", "size": 8734, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/test.py", "max_stars_repo_name": "quangnhat185/RTM3D", "max_stars_repo_head_hexsha": "da657e9d8f1499eeedf222fdf0397c90e04eb877", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_... |
/****************************************************************
** **
** Copyright(C) 2020 Quanergy Systems. All Rights Reserved. **
** Contact: http://www.quanergy.com **
** ... | {"hexsha": "a2407d9dd749c9659139a9138c6a8338ada5fed0", "size": 2177, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/client/device_info.cpp", "max_stars_repo_name": "nobleo/quanergy_client", "max_stars_repo_head_hexsha": "5d347041a5bcfaa063f01ab87d6784a48a4f2b9b", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
pyrealsense2 OSGAR wrapper
"""
import math
import logging
import threading
import numpy as np
from osgar.lib.quaternion import conjugate as quaternion_inv, euler_to_quaternion, multiply as quaternion_multiply, rotate_vector
g_logger = logging.getLogger(__name__)
try:
import pyrealsense2 as rs
try:
... | {"hexsha": "2def641357d8973d6f3833d55f397db6ad4cc41e", "size": 18627, "ext": "py", "lang": "Python", "max_stars_repo_path": "osgar/drivers/realsense.py", "max_stars_repo_name": "robotika/osgar", "max_stars_repo_head_hexsha": "6f4f584d5553ab62c08a1c7bb493fefdc9033173", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import tensorflow as tf1
import pandas as pd
import numpy as np
from detectors.nn_tmd import NeuralNetworkTMD
from tensorflow.contrib.rnn import BasicLSTMCell, MultiRNNCell, DropoutWrapper
class RecurrentNeuralNetworkTMD(NeuralNetworkTMD):
"""
Wrapper that uses TensorFlow to allow training and
using a LST... | {"hexsha": "91066560f05a3ef208162d66ad9c84f84dc9ea4b", "size": 5832, "ext": "py", "lang": "Python", "max_stars_repo_path": "detectors/rnn_tmd.py", "max_stars_repo_name": "eltonfss/TMDLibrary", "max_stars_repo_head_hexsha": "2eaca21d61ae4d012435c2dc8b65be0b62a2afd3", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
module lmv
"""
Export matrices and vectors to disk so that they can be read by the code dcsrmv.
This code performs sparse CSR matrix- dense vector multiplication on the GPU.
"""
using SparseArrays
function write_vector(filename::String, u::Array{T, 1};
verbose::Bool=false) whe... | {"hexsha": "305a7ded9f28107031d3e7e82a6499db6ca394bb", "size": 4624, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lmv.jl", "max_stars_repo_name": "ooreilly/sbpjl", "max_stars_repo_head_hexsha": "739883b9f4a752a63e6363a084e8db87251852b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_... |
theory Compiler imports Compiler1 Compiler2 begin
definition J2JVM :: "'addr J_prog \<Rightarrow> 'addr jvm_prog"
where [code del]: "J2JVM \<equiv> compP2 \<circ> compP1"
lemma J2JVM_code [code]:
"J2JVM = compP (\<lambda>C M Ts T (pns, body). compMb2 (compE1 (this#pns) body))"
by(simp add: J2JVM_def compP2_def o_de... | {"author": "Josh-Tilles", "repo": "AFP", "sha": "f4bf1d502bde2a3469d482b62c531f1c3af3e881", "save_path": "github-repos/isabelle/Josh-Tilles-AFP", "path": "github-repos/isabelle/Josh-Tilles-AFP/AFP-f4bf1d502bde2a3469d482b62c531f1c3af3e881/thys/JinjaThreads/Compiler/Compiler.thy"} |
from __future__ import print_function
from __future__ import division
import numpy as np
import tensorflow as tf
import h5py
import json
from data import UnicodeCharsVocabulary, ElmoBatcher
DTYPE = 'float32'
DTYPE_INT = 'int64'
class BidirectionalLanguageModel(object):
def __init__(
self,
options_file... | {"hexsha": "7c1c1a6b9a5d28a276886a51f5f9e465ed61d5bf", "size": 23059, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/models/bilm/model.py", "max_stars_repo_name": "strubell/Parser", "max_stars_repo_head_hexsha": "c8a7f69a5f985c0e57419333f1d33015191dfb92", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# Copyright 2017 Google Inc. 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 applicable law or... | {"hexsha": "24d0b5c165680d46bc02643534274e9124a35c7c", "size": 7088, "ext": "py", "lang": "Python", "max_stars_repo_path": "_py2tmp/ir0_optimization/_recalculate_template_instantiation_can_trigger_static_asserts_info.py", "max_stars_repo_name": "google/tmppy", "max_stars_repo_head_hexsha": "faf67af1213ee709f28cc5f492ec... |
(** * Hoare2: Hoare Logic, Part II *)
Set Warnings "-notation-overridden,-parsing".
Require Import Coq.Bool.Bool.
Require Import Coq.Arith.Arith.
Require Import Coq.Arith.EqNat.
Require Import Coq.omega.Omega.
From PLF Require Import Maps.
From PLF Require Import Imp.
From PLF Require Import Hoare.
(* ###############... | {"author": "kolya-vasiliev", "repo": "programming-language-foundations-2018", "sha": "f1f8daf251503979ec11a9e206515af07924321a", "save_path": "github-repos/coq/kolya-vasiliev-programming-language-foundations-2018", "path": "github-repos/coq/kolya-vasiliev-programming-language-foundations-2018/programming-language-found... |
#include <iostream>
#include <boost/network/protocol/http/client.hpp>
#include <webmock/api.hpp>
#include <webmock/adapter/cpp_netlib.hpp>
int main() {
namespace webmock_adapter = webmock::adapter::cpp_netlib;
using namespace webmock::api::directive;
constexpr bool enabled_webmock = true;
namespac... | {"hexsha": "b4f85f94e1fcf2c204f6fb1f61428255ca9c284b", "size": 1215, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/basic.cpp", "max_stars_repo_name": "mrk21/cpp-webmock", "max_stars_repo_head_hexsha": "13a7b8362e2e84d47de45071956a43ac3005c58a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0,... |
import numpy as np
ACTIONS_MAP = {
0: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
1: [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
2: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
3: [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
4: [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
5: [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
6: [0, 0,... | {"hexsha": "db00afa0c353de0eb343c859208f09bb596c9183", "size": 3174, "ext": "py", "lang": "Python", "max_stars_repo_path": "sfii_agent_base/actuator.py", "max_stars_repo_name": "mad-rl/sfii-challenge", "max_stars_repo_head_hexsha": "f69ccc49b8fcbb505fe06c994669d272a44ebb26", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
# Copyright 2019 Markus Marks
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | {"hexsha": "7b0b77bd31fdaf5fbb09e2b00abfc330a4c28cec", "size": 6932, "ext": "py", "lang": "Python", "max_stars_repo_path": "mnist/plotting.py", "max_stars_repo_name": "limberc/hypercl", "max_stars_repo_head_hexsha": "ad098a3b18cf2a2ae6e3ecd28a2b7af698f7b807", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "839513d3ac20e2b6a3d6df3f0be3f477c38775e4", "size": 27829, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/tfflat/base.py", "max_stars_repo_name": "Ascend-Huawei/PoseFix", "max_stars_repo_head_hexsha": "9b287934879beadc71daa3a642cbbb4a0feb1db5", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
#include <iostream>
#include <fstream>
#include <Eigen>
using namespace std;
using namespace Eigen;
// function definitions for reading matrices from .txt file
void read_sparse_matrix(const std::string& filename, SparseMatrix<float, RowMajor>& matrix);
void read_matrix(std::string file, MatrixXf& matrix);
int main()... | {"hexsha": "a4a4c5ff25dfb65c9717383e825eff3e30798aed", "size": 3362, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "source/main.cpp", "max_stars_repo_name": "ROMSOC/benchmark_adaptive-optics", "max_stars_repo_head_hexsha": "38933fa2eacaf4ff3c5f5f1c7d25b13e77880f47", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\section{Apparatus}
The measurement setup consists of a turntable with an integrated photo-gate
system used for time measurements.
\singlespacing
\begin{itemize}
\item sample
\item turntable
\item photo gate holder
\item pulley
\item string
\item weight
\item photo gate
\item shielding pin
\item cone pulley
\item level... | {"hexsha": "5dbf10202bec9132f99679c38a78c6b8040b35c0", "size": 2624, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "E1/part/apparatus.tex", "max_stars_repo_name": "iamwrm/VP141", "max_stars_repo_head_hexsha": "c0a5d1992967b1552d6f7ea0806c9244d58f64ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
_pr(t::Dict, x::Int, y::Int, z::Int) = join((rstrip(join(t[(n, m)] for n in range(0, 3+x+z))) for m in reverse(range(0, 3+y+z))), "\n")
function cuboid(x::Int, y::Int, z::Int)
t = Dict((n, m) => " " for n in range(0, 3 + x + z), m in range(0, 3 + y + z))
xrow = vcat("+", collect("$(i % 10)" for i in range(0, x... | {"hexsha": "2e9f820dfee4cd4722b865ac1279bc51bc772c44", "size": 903, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/draw-a-cuboid.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max... |
###########################################
# Functions for formatting TRUST BCR data #
###########################################
from __future__ import print_function
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
from deep_bcr import *
#########################################... | {"hexsha": "c7013c561ecff4519422504b0944709886a84cef", "size": 26940, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tcga_bcr.py", "max_stars_repo_name": "huhansan666666/deepbcr", "max_stars_repo_head_hexsha": "f476acf6862283bd304bf49d748503353eb60135", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
from .optools import precompute_ops
from .cy.tensorutils import atensorcontract
from .cy.wftools import reshape_wf
def compute_expect(nel, nmodes, nspfs, npbfs, spfstart, spfend, psistart,
psiend, psi, op, pbfs):
"""Computes the expectation value of a generic operator.
NOT... | {"hexsha": "ef0fd74435ffe85b6d663386e59355fe5a1200d2", "size": 5210, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymctdh/expect.py", "max_stars_repo_name": "addschile/pymctdh", "max_stars_repo_head_hexsha": "20a93ce543526de1919757defceef16f9005f423", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
######################################################################################
# Roberts advection test for IMEX sdc in Paralpha with just an explicit part and Euler
# no spatial variables here, just time
######################################################################################
import numpy as np
... | {"hexsha": "adc6d98d2a208fd00a0a23ff9c8b065d52788543", "size": 2941, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySDC/playgrounds/paralpha/explicit.py", "max_stars_repo_name": "brownbaerchen/pySDC", "max_stars_repo_head_hexsha": "31293859d731646aa09cef4345669eac65501550", "max_stars_repo_licenses": ["BSD-2-... |
"""
Copyright 2020 The OneFlow 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 applicable law or agr... | {"hexsha": "ed6df8b52fcceea3b556efd53793fd245ed83723", "size": 3785, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/oneflow/nn/modules/gather.py", "max_stars_repo_name": "Warmchay/oneflow", "max_stars_repo_head_hexsha": "5a333ff065bb89990318de2f1bd650e314d49301", "max_stars_repo_licenses": ["Apache-2.0"]... |
# -*- coding: utf-8 -*-
import pyfits
from pylab import *
import Marsh
import numpy
import scipy
def getSpectrum(filename,b,Aperture,minimum_column,maximum_column):
hdulist = pyfits.open(filename) # Here we obtain the image...
data=hdulist[0].data # ... and we obtain the... | {"hexsha": "b53e8dd7542738ba00d7fff6a0aa39dae1fd8173", "size": 1329, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/OptExtract/TestSimple.py", "max_stars_repo_name": "afeinstein20/ceres", "max_stars_repo_head_hexsha": "e55150c587782cbecfd45c21ba0ce0023e54c3a9", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from .engine import Engine
from base import View as BaseView, Controller as BaseController, run
from network import Network
import sys
import numpy as np
class View(BaseView, view_type=Engine):
"""Viewer for :py:class:`Engine`.
Attributes
----------
_engine: Engine
The engine controlled by t... | {"hexsha": "3164f8edbce552fa124c8a5a59a2a6fd1aeca86d", "size": 2357, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/am/controller.py", "max_stars_repo_name": "Petr-By/qtpyvis", "max_stars_repo_head_hexsha": "0b9a151ee6b9a56b486c2bece9c1f03414629efc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy
from sklearn.datasets import load_iris
#loading iris data set
iris=load_iris()
#to print
print(iris.feature_names)
print(iris.target_names)
#training data
#features data
print(iris.data)
#target data means flowers data
print(iris.target)
#now splitting into test and train data sets
from sklearn.model_sele... | {"hexsha": "fcb13bd69f4bc958b0da601fa42d0eea2e95e339", "size": 1184, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_iris1.py", "max_stars_repo_name": "rahul-1918/supervised_ML", "max_stars_repo_head_hexsha": "0081021190a8947626e69bf2c8d5655db49c6a13", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
[STATEMENT]
lemma least_max_arg_max_enum_correct1:
assumes "X \<noteq> {}"
shows "fst (least_max_arg_max_enum (f :: _ \<Rightarrow> (_ :: linorder)) X) = (LEAST x. is_arg_max f (\<lambda>x. x \<in> X) x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fst (least_max_arg_max_enum f X) = (LEAST x. is_arg_max f (... | {"llama_tokens": 1089, "file": "MDP-Algorithms_code_Code_DP", "length": 8} |
/***********************************************************************************
* Copyright (c) 2016, UT-Battelle
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions o... | {"hexsha": "efd5cef9994ec579f292da5e4faff8603b9cea7c", "size": 5838, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "compiler/FermionCompiler.cpp", "max_stars_repo_name": "zpparks314/xacc-vqe", "max_stars_repo_head_hexsha": "37aaadb12d856324532c42ca9f7e56147edbd2e7", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import abc
import torch
import torch.optim as optim
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from .networks import FCNN
class IVP:
"""
A initial value problem:
x (t=t_0) = x_0
x'(t=t_0) = x_0_prime
"""
def __init__(self, t_0, x_0, x_0_prime=Non... | {"hexsha": "8b68231565417804738df354b17fe5b0c0300b79", "size": 9291, "ext": "py", "lang": "Python", "max_stars_repo_path": "neurodiffeq/ode.py", "max_stars_repo_name": "gnicks007/neurodiffeq", "max_stars_repo_head_hexsha": "a4a4fd2379442937b748712e1cf45510aba6f0c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import CandyCrashSimulation3X3_2 as f
import random
import copy
import numpy as np
width = height = 3
s = 2 ** (width * height)
num_candy = 2
N = 100000
print("size: {} * {}".format(width,height))
w_score = np.zeros(s*N).reshape(s,N)
for i in range(s):
board = f.buildboard(i, height, width)
for ... | {"hexsha": "bdff264a79d88894078bf8a6f01bbf6ea33500e3", "size": 676, "ext": "py", "lang": "Python", "max_stars_repo_path": "9X9_board_Expectation.py", "max_stars_repo_name": "james60708/Candy-Crash-Simulation", "max_stars_repo_head_hexsha": "e346b7af6039cf3fe26a5f71e7fbd818a8480252", "max_stars_repo_licenses": ["MIT"], ... |
from typing import Callable
import torch
import torch.nn as nn
import numpy as np
from chemprop.args import TrainArgs
def get_loss_func(args: TrainArgs) -> Callable:
"""
Gets the loss function corresponding to a given dataset type.
:param args: Arguments containing the dataset type ("classification", "... | {"hexsha": "eaadf0eac6b613f50a91a78fd214e5c34bc586fc", "size": 14800, "ext": "py", "lang": "Python", "max_stars_repo_path": "chemprop/train/loss_functions.py", "max_stars_repo_name": "davidegraff/chemprop", "max_stars_repo_head_hexsha": "b1f342255f26d40b65c34f1260297d2b772b98b2", "max_stars_repo_licenses": ["MIT"], "ma... |
# Standard libs
import math
from typing import Dict, List, Optional, Tuple, Union
# Third party libs
import click
import numpy as np
import vpype as vp
import vpype_cli
from shapely.geometry import LineString, Polygon, MultiLineString
from shapely.strtree import STRtree
def add_to_linecollection(lc, line):
"""He... | {"hexsha": "9acf9ec7afd3b34e605e6bf88e727431fbde619e", "size": 6471, "ext": "py", "lang": "Python", "max_stars_repo_path": "occult/occult.py", "max_stars_repo_name": "St0rmingBr4in/occult", "max_stars_repo_head_hexsha": "6afcaa7c31221b500957f04bc51dd00e9f003b7a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
from __future__ import absolute_import, division, print_function
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
from PIL import Image
from PIL import ImageOps
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
def plot_image(i, predictions_array, true_label, img):
... | {"hexsha": "b25f5316119c491b48b7c790ac42f81de66d4c5f", "size": 3011, "ext": "py", "lang": "Python", "max_stars_repo_path": "labs/lab-10/checkpoint3.py", "max_stars_repo_name": "zlmcdaniel/oss-repo-template", "max_stars_repo_head_hexsha": "32722e902a004fd050a5e870eb2a303787a851eb", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env Rscript
pkgs <- commandArgs(trailingOnly = TRUE)
# set libPath to pwd
lib <- .libPaths()
if(file.access(lib[1], mode=2)!=0){
pwd <- getwd()
pwd <- file.path(pwd, "lib")
dir.create(pwd)
.libPaths(c(pwd, lib))
}
if(length(pkgs)>0){
while(!requireNamespace("BiocManager", quietly = TRUE)){
ins... | {"hexsha": "a8ddaa9b1bebdd9fa38c9372c22fe88f82e4c8a2", "size": 788, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/install_packages.r", "max_stars_repo_name": "jianhong/universalModule", "max_stars_repo_head_hexsha": "032d77865ec17fcf055f5db61a00dd5c76478e05", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
function [lp,dlp] = priorDeltaMulti(x)
% Dummy hyperparameter prior distribution to fix the value of a hyperparameter.
% The function is not intended to be evaluated but exists merely to make
% the user aware of the possibility to use it. The function is equivalent
% to priorClampedMulti.
%
% For more help on design o... | {"author": "benfulcher", "repo": "hctsa", "sha": "919f2aed7cc8e1a3a03304c1ade573fa664c73f8", "save_path": "github-repos/MATLAB/benfulcher-hctsa", "path": "github-repos/MATLAB/benfulcher-hctsa/hctsa-919f2aed7cc8e1a3a03304c1ade573fa664c73f8/Toolboxes/gpml/prior/priorDeltaMulti.m"} |
[STATEMENT]
lemma while_vrt_hoare_r [hoare_safe]:
assumes "\<And> z::nat. \<lbrace>p \<and> b \<and> v =\<^sub>u \<guillemotleft>z\<guillemotright>\<rbrace>S\<lbrace>p \<and> v <\<^sub>u \<guillemotleft>z\<guillemotright>\<rbrace>\<^sub>u" "`pre \<Rightarrow> p`" "`(\<not>b \<and> p) \<Rightarrow> post`"
shows "\<l... | {"llama_tokens": 459, "file": "UTP_utp_utp_hoare", "length": 4} |
"""Copyright © 2020-present, Swisscom (Schweiz) AG.
All rights reserved."""
import numpy as np
from dataset.dataset_loader import Dataset
from inference_models.inference_torch import InferenceTorch
from inference_models.__inference_utils import compute_percent_correct
from codi.nlp_trainer import NLPTrainer
import... | {"hexsha": "3bf31441187ea2945e04ac79f4c73b825c70cffa", "size": 5318, "ext": "py", "lang": "Python", "max_stars_repo_path": "n_step_experiment.py", "max_stars_repo_name": "swisscom/ai-research-data-valuation-repository", "max_stars_repo_head_hexsha": "bcb45b7d8b84674f12e0a3671260290d98257c9f", "max_stars_repo_licenses":... |
import numpy as np
from plaidrl.envs.pearl_envs.ant_multitask_base import MultitaskAntEnv
class AntDirEnv(MultitaskAntEnv):
def __init__(
self,
task=None,
n_tasks=2,
fixed_tasks=None,
forward_backward=False,
direction_in_degrees=False,
**kwargs
):
... | {"hexsha": "6b165b532b45e1fc6d9ab85f6aa30cfba119ac83", "size": 2926, "ext": "py", "lang": "Python", "max_stars_repo_path": "plaidrl/envs/pearl_envs/ant_dir.py", "max_stars_repo_name": "charliec443/plaid-rl", "max_stars_repo_head_hexsha": "2e8fbf389af9efecd41361df80e40e0bf932056d", "max_stars_repo_licenses": ["MIT"], "m... |
"""
MAP vs MLE
"""
import pytest
import numpy as np
import pandas as pd
import sys
import os
sys.path.insert(0, os.path.abspath("."))
sys.path.insert(0, os.path.abspath("../"))
from BlackBox_Python import MapVMle as MVM
# test inputs
def test_input_types():
try:
MVM.getMLE()
except TypeError:
... | {"hexsha": "b8920ef74ffdd9f607214b5280e10a2debcb203e", "size": 1470, "ext": "py", "lang": "Python", "max_stars_repo_path": "BlackBox_Python/tests/test_MAP_MLE.py", "max_stars_repo_name": "UBC-MDS/BlackBox_Python", "max_stars_repo_head_hexsha": "5eb7effa09d21b5fe0ca8a2bb18a456d1e6edcc8", "max_stars_repo_licenses": ["MIT... |
__precompile__(true)
module DistributedArrays
using Compat
if VERSION >= v"0.5.0-dev+4340"
using Primes
using Primes: factor
end
if VERSION < v"0.5.0-"
typealias Future RemoteRef
typealias RemoteChannel RemoteRef
end
importall Base
import Base.Callable
import Base.BLAS: axpy!
export (.+), (.-), (.... | {"hexsha": "baa517e6c4c5cdefa0bc9e72b7c13e2d2ab14666", "size": 40396, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DistributedArrays.jl", "max_stars_repo_name": "mbauman/DistributedArrays.jl", "max_stars_repo_head_hexsha": "1a317427121042c24f9c2facaec51734e2274874", "max_stars_repo_licenses": ["MIT"], "max... |
#pip3 install opencv-python
import tensorflow as tf
import scipy.misc
import model
import cv2
from subprocess import call
import math
sess = tf.InteractiveSession()
saver = tf.train.Saver()
saver.restore(sess, "save/model.ckpt")#Here we are loading our trained model
img = cv2.imread('steering_wheel_image.jpg',0) #lo... | {"hexsha": "739a98c9ae070b52a7cc21f5a066f459c42fd1d4", "size": 2534, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_dataset.py", "max_stars_repo_name": "Vermankayak/Self-Driving-Car", "max_stars_repo_head_hexsha": "42992df96b72c69a5d2a5139f3d8f97e39acae9b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#PLotter_V0.11 _ Added original_file_loc to top
# Usage: python3 Cov_plotter.py <Contract file name> <Contract Name> <Saving folder> <original file location> <Results_folder> <ID> <vuln file location> <Fuzzer>
import json
import os
import sys
from decimal import Decimal
import sys
from openpyxl import load_workbook
im... | {"hexsha": "c8072f6e5c7580b8801b1f83c8f5b60de70eb37c", "size": 6976, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/Cov_plotter.py", "max_stars_repo_name": "sunbeam891/Smart_contract_fuzzing", "max_stars_repo_head_hexsha": "327873f562028fb3ea241fb0c1dc0f039e8c005d", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
lemma (in Ring) one_m_x_times:"x \<in> carrier R \<Longrightarrow>
(1\<^sub>r \<plusminus> -\<^sub>a x) \<cdot>\<^sub>r (nsum R (\<lambda>j. x^\<^bsup>R j\<^esup>) n) = 1\<^sub>r \<plusminus> -\<^sub>a (x^\<^bsup>R (Suc n)\<^esup>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> carrier R \<Long... | {"llama_tokens": 3095, "file": "Valuation_Valuation2", "length": 9} |
subroutine ataylr(delxi,daffp0,nord,nordmx,npt,nptmax,affp0,affp)
c
c This subroutine evaluates Taylor's series expansions for phase
c affinities. These expansions are used to find phase boundaries
c at which new phases appear in the ES.
c
c Compare with:
c
c EQ6/ptaylr.f
c EQ6/rtaylr.... | {"hexsha": "b76c497a633fa560b7630c6c9be486af3aa2e82f", "size": 1899, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/eq6/src/ataylr.f", "max_stars_repo_name": "39alpha/eq3_6", "max_stars_repo_head_hexsha": "4ff7eec3d34634f1470ae5f67d8e294694216b6e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
#coding:utf-8
import argparse
import os
import ast
import paddle.fluid as fluid
import paddlehub as hub
import numpy as np
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=1, help="Number of epoches for fine-... | {"hexsha": "40e170a564ddcc9c54a6d6aff08e898466da5320", "size": 3556, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/image_classification/img_classifier.py", "max_stars_repo_name": "jjandnn/PaddleHub", "max_stars_repo_head_hexsha": "05e24ce2495b60e095b1b0d94ba1c7ac09d077f9", "max_stars_repo_licenses": ["Apa... |
function mbasis = basis_matrix_overhauser_uni_r ( )
%*****************************************************************************80
%
%% BASIS_MATRIX_OVERHAUSER_UNI_R sets up the right uniform Overhauser spline basis matrix.
%
% Discussion:
%
% This basis matrix assumes that the data points P(N-2), P(N-1),
% a... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/spline/basis_matrix_overhauser_uni_r.m"} |
(* Title: ZF/Induct/ListN.thy
Author: Lawrence C Paulson, Cambridge University Computer Laboratory
Copyright 1994 University of Cambridge
*)
section \<open>Lists of n elements\<close>
theory ListN imports ZF begin
text \<open>
Inductive definition of lists of \<open>n\<close> elements; see
\... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/ZF/Induct/ListN.thy"} |
#!/usr/bin/env python
import time
from utils.data_loader import MRI_Loader
from utils.callbacks import Metrics_Conversion_Risk, LR_Plateau
from utils.preprocess import Stratified_KFolds_Generator, Train_Test_Split, One_Hot_Encode
from utils.models import MudNet
from utils.plot_metrics import plot_metrics
from tensor... | {"hexsha": "1b7a4ac745e8925db7407a1a6378b06263fbce15", "size": 4680, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "SijRa/mri-analysis", "max_stars_repo_head_hexsha": "a35411bda6e39eff57f715a695b7fb6a30997706", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_sta... |
[STATEMENT]
lemma ccField_ccBind: "ccField (ccBind v e\<cdot>(ae,G)) \<subseteq> fv e"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ccField (ccBind v e\<cdot>(ae, G)) \<subseteq> fv e
[PROOF STEP]
by (auto simp add: ccBind_eq dest: subsetD[OF ccField_cc_restr]) | {"llama_tokens": 111, "file": "Call_Arity_CoCallAnalysisBinds", "length": 1} |
using RvSpectML
using CSV
function calc_rvs_from_ccf_total(ccfs::AbstractArray{T1,2}, pipeline::PipelinePlan; v_grid::AbstractVector{T2}, times::AbstractVector{T3},
recalc::Bool = false, verbose::Bool = true) where {T1<:Real, T2<:Real, T3<:Real }
@assert length(v_grid) == size(ccfs,... | {"hexsha": "d39c9763e29bcb0e8d8b550da7890dcb770b18a1", "size": 1446, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples_old/old/scripts/rvs_from_ccf_total.jl", "max_stars_repo_name": "alexander-wise/RvSpectML.jl", "max_stars_repo_head_hexsha": "8fd030f4a8b6478193ed36be7a3174cd2ea7b5aa", "max_stars_repo_lice... |
Load LFindLoad.
Load LFindLoad.
From adtind Require Import goal84.
From lfind Require Import LFind.
Lemma lfind_state (x:natural) (y:natural) (z:natural) (IHx:@eq natural (mult (plus x y) z) (plus (mult x z) (mult y z))):@eq natural (plus (mult x z) (plus (mult y z) z))
(plus (plus (mult x z) z) (mult y z)).
Admitt... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal84_distrib_96_plus_commut/lfind_qui... |
import os
import sys
import glob
import argparse
import numpy as np
import SimpleITK as sitk
import torch
from segmentation.utils.DataReader import DataReader
from segmentation.eval.TestNet import TestNet
import warnings
if not sys.warnoptions:
warnings.simplefilter("ignore")
def arguments():
parser = argp... | {"hexsha": "cff742a96f2244adc0a8f990a679d8e700145870", "size": 3331, "ext": "py", "lang": "Python", "max_stars_repo_path": "segmentation/eval/eval.py", "max_stars_repo_name": "enjoy-the-science/brain-texts", "max_stars_repo_head_hexsha": "2f90cff6b7efd610791b278579c62ba802eb0f02", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from scipy import stats
import os
import importlib
import DeepNetPI
import DataGen
import utils
from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score
os.environ['TF_CPP_MIN_LOG_LEVEL']='2' # avoids a warning
importlib.reload(DeepNetPI)
impo... | {"hexsha": "4aacea1322116a95027d2da78cfce03ea687b9ce", "size": 13791, "ext": "py", "lang": "Python", "max_stars_repo_path": "ICLR_2022/Cubic_10D/QD/QD_cubic10D.py", "max_stars_repo_name": "streeve/PI3NN", "max_stars_repo_head_hexsha": "f7f08a195096e0388bb9230bc67c6acd6f41581a", "max_stars_repo_licenses": ["Apache-2.0"]... |
#ifndef ROUTE_STEP_HPP
#define ROUTE_STEP_HPP
#include "extractor/travel_mode.hpp"
#include "engine/guidance/step_maneuver.hpp"
#include "util/coordinate.hpp"
#include "util/guidance/bearing_class.hpp"
#include "util/guidance/entry_class.hpp"
#include "extractor/guidance/turn_lane_types.hpp"
#include "util/guidance/t... | {"hexsha": "29fb3a7b12b08aed34e2a0a69a077d16fd87f820", "size": 6198, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/engine/guidance/route_step.hpp", "max_stars_repo_name": "jhermsmeier/osrm-backend", "max_stars_repo_head_hexsha": "7b11cd3a11c939c957eeff71af7feddaa86e7f82", "max_stars_repo_licenses": ["BSD... |
import numpy as np
SEED = 1234
import random
random.seed(SEED)
np.random.seed(SEED)
import os
os.environ["PYTHONHASHSEED"] = str(SEED)
############ my classes ##################
import Preprocessor
########## other packages ################
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
f... | {"hexsha": "a2889e18caa74181769c2cea7faae64499c56f47", "size": 6934, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "PantelisKyriakidis/Advanced_ML_topics", "max_stars_repo_head_hexsha": "394a074de21d6bbeb5e4eae3529cd505e077adfb", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import sys
import utils
def render_frame(num, data, line):
xyz_file = open(files[num])
_x = []
_y = []
for xyz in xyz_file:
_x.appe... | {"hexsha": "56620678ab7badf0457e2e7a39136c73f58b2608", "size": 1064, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/visualize_xyz.py", "max_stars_repo_name": "PavelBlend/fluid-engine-dev", "max_stars_repo_head_hexsha": "45b4bdbdb4c6d8c0beebc682180469198203b0ef", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np;
import libs.mpyc as reg;
def initialize_mpc():
# System matrices
A = np.matrix([[0.968022]]);
B = np.matrix([[0.000154882,0.00332278,0.000285817]]);
C = np.matrix([[55811.1],[0.315664]]);
D = np.matrix([[0,0,0],[0,0,0]]);
# Control parameters
L = 4;
Q = np.diag(np.t... | {"hexsha": "216d4fbff42110c0c66dda42e4332a38419b5b4e", "size": 1119, "ext": "py", "lang": "Python", "max_stars_repo_path": "SAVE/code/ctls/mpc.py", "max_stars_repo_name": "ManCla/ESEC-FSE-2020", "max_stars_repo_head_hexsha": "5284c306a52418c49636aa951934756babac027d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
%% subplotPPT _ Demo script
% Demonstrates different ways in which the function subplotPPT can work
%
% Copyright 2008, The MathWorks, Inc. MATLAB and Simulink are registered
% trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a
% list of additional trademarks. Other product or brand names may be ... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/20949-subplotppt/Test_PPTScript.m"} |
from model import *
import torch
from PIL import Image
from torchvision import transforms
from torch.autograd import Variable
import h5py
import numpy as np
import os
from tqdm import tqdm
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Loadinging model...\n')
model = CovidNet(bna=True, b... | {"hexsha": "6160109862076745e6db624915b12cf75005f63e", "size": 3416, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/recognize/code/test.py", "max_stars_repo_name": "yccye/CT_AI_web", "max_stars_repo_head_hexsha": "267553d3aaaef78f7dbdd652c0f1868ec60862c2", "max_stars_repo_licenses": ["MulanPSL-1.0"], "ma... |
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
import numpy
import tflearn
import nltk
nltk.download('punkt')
import tensorflow
import random
import json
import pickle
# Load data to train the model
with open('intents.json') as file:
data = json.load(file)
try:
with open("data.pi... | {"hexsha": "29c81a44b8d284dcafaaf6b6b4d7a9c375e28f4b", "size": 3873, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "marcelotm23/Python-AI-Chat-Bot", "max_stars_repo_head_hexsha": "b0fe5f448bd54f518ea274bf4e07b3e047faff04", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
* Jacobi.f
* computes the variables for an elementary Jacobi rotation
* code adapted from the "Handbook" routines for complex M12
* (Wilkinson, Reinsch: Handbook for Automatic Computation, p. 202)
* this file is part of FormCalc
* last modified 15 Jun 04 th
subroutine Jacobi(delta, s, tau, diff, M12, absM12)
implic... | {"hexsha": "f2f25ca853e74d1a7b8bf4cb7084599a29b3d1f7", "size": 786, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Externals/micromegas_4.3.5/CPVMSSM/lib/nngg12/util/diag/Jacobi.f", "max_stars_repo_name": "yuanfangtardis/vscode_project", "max_stars_repo_head_hexsha": "2d78a85413cc85789cc4fee8ec991eb2a0563ef8", ... |
using PyPlot
using CSV
include("structures.jl")
include("utilitaires.jl")
include("parser.jl")
include("concorde.jl")
include("partitionnement_mono.jl")
function main()
# Paramètres
vitesseCamion = 1
vitesseDrone = 3
listeInstances = readdir("experimentations")
nbInstances = length(listeInstances)... | {"hexsha": "b9a13c319d278c445374dd0b85ebf1e64f3cb55e", "size": 15598, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "graphiques.jl", "max_stars_repo_name": "xgandibleux/tspd-solver", "max_stars_repo_head_hexsha": "63f4f4b955ec433c382b5836f9d9d93004fb3889", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import os
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
from sklearn.utils.random import sample_without_replacement
from sklearn.model_selection import train_test_split
from utils.datasets import ROOT_DATA_DIR... | {"hexsha": "8962ed5a83c4ae49cbe5a7f17acc5b8814d4a6a2", "size": 2920, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/datasets/c3.py", "max_stars_repo_name": "inJeans/c3dis-utils", "max_stars_repo_head_hexsha": "c5514915f6d54c63c6fdb5cec64aabe19720a793", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import torch
import torch.nn.functional as F
from ..box_utils import decode, jaccard, index2d
from utils import timer
from data import cfg, mask_type
import numpy as np
import pyximport
pyximport.install(setup_args={"include_dirs":np.get_include()}, reload_support=True)
from utils.cython_nms import nms as cnms
cl... | {"hexsha": "07b212848964811baf7a1701187254021de143d7", "size": 8523, "ext": "py", "lang": "Python", "max_stars_repo_path": "layers/functions/detection.py", "max_stars_repo_name": "Agade09/yolact", "max_stars_repo_head_hexsha": "6e38a10bd92fb8d0ba5cad124b691ca161dfde32", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
'''
Author: Hans Erik Heggem
Email: hans.erik.heggem@gmail.com
Project: Master's Thesis - Autonomous Inspection Of Wind Blades
Repository: Master's Thesis - CV (Computer Vision)
'''
import numpy as np
from src.bin.tools import CheckDir
from src.DroneVision.DroneVision_src.hardware.RecordFrames import RecordFrames
... | {"hexsha": "072a8fafb7447264087eb7cec1c6607b558114a0", "size": 3368, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/DataBase/FrameRecorder/FrameRecorder.py", "max_stars_repo_name": "swipswaps/Wind-Blade-Inspection", "max_stars_repo_head_hexsha": "24613b9f2fbab224ffd3bbfc1082aa1547c19779", "max_stars_repo_li... |
# This script does the data prep for the us data in the ultra competition gender gap paper
# Importing required modules
import pandas as pd
import numpy as np
# Declaring the filepath where the data was stored by the scraper
username = ''
filepath = 'C:/Users/' + username + '/Documents/Data/ultracompetiti... | {"hexsha": "cb16eaff90b234d10cdf9c340b55cd7bb91d6c6b", "size": 8095, "ext": "py", "lang": "Python", "max_stars_repo_path": "ultracompetitive_data_prep.py", "max_stars_repo_name": "cat-astrophic/ultracompetitive", "max_stars_repo_head_hexsha": "f13c5551ec42fbd186149144363f9e8b2142b5d2", "max_stars_repo_licenses": ["MIT"... |
import pandas as pd
import os
import pickle as pkl
import tldextract
from tqdm import tqdm
import statistics
from collections import Counter
import numpy as np
## SCIENCE - DOMAIN SCORE CALCULATION
filespath='../data/user-domains-extracted'
# Load our seed set of pay-level domains sourced from Media Bias Fact Check
... | {"hexsha": "4be09c7ad368751c0e1924e2ca0173747eed7d7d", "size": 10054, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/Domain-Score-Calc.py", "max_stars_repo_name": "ashwinshreyas96/Multidimensional-Ideological-Polarization", "max_stars_repo_head_hexsha": "9f761877f4f4e4f59e7472c29af03e415d271ff5", "max_star... |
import os, cv2, glob
import numpy as np
import torch
import torch.utils.data as data
from torchvision import datasets, models, transforms
from utils import get_classes, get_boxed_train_ids, get_val_ids, load_bbox_dict, load_small_train_ids, get_class_names, get_test_ids
from encoder import DataEncoder
import settings
... | {"hexsha": "2aac32de16f043f11e65295864b31add3f0c4987", "size": 6040, "ext": "py", "lang": "Python", "max_stars_repo_path": "imgdataset.py", "max_stars_repo_name": "chicm/detect", "max_stars_repo_head_hexsha": "c1b611344d102fd7e94d94c678a44339e18ddd21", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
from os.path import join, dirname
sys.path.insert(0, join(dirname(__file__), '..'))
sys.path.insert(0, join(dirname(__file__), '../../'))
sys.path.insert(0, join(dirname(__file__), '../../../'))
import os
import time
import random
import argparse
import numpy ... | {"hexsha": "9d5f1538c1af1b56677985f03e09f11f8edd57e4", "size": 7527, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/model_uncertainty/Ours/test-tSNE.py", "max_stars_repo_name": "Czworldy/GP_traj", "max_stars_repo_head_hexsha": "96261f39a5a322092e3a6be98938bb4601f0f746", "max_stars_repo_licenses": ["MIT"... |
module Compiler.Erlang.Erlang
import Compiler.Common
import Compiler.CompileExpr
import Compiler.Erlang.GlobalOpts
import Compiler.Erlang.ModuleOpts
import Compiler.Erlang.Cmd
import Compiler.Erlang.Name
import Compiler.Erlang.Codegen.NamedCExpToErlExpr
import Compiler.Erlang.Codegen.NamedCExpToErlExpr.RtsSupport
imp... | {"hexsha": "b12850978531fa9c2bee9278663d188a0edb131f", "size": 9811, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "idris2/src/Compiler/Erlang/Erlang.idr", "max_stars_repo_name": "Qqwy/Idris2-Erlang", "max_stars_repo_head_hexsha": "945f9c12d315d73bfda2d441bc5f9f20696b5066", "max_stars_repo_licenses": ["BSD-3-Cl... |
import io
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, DrawingArea, HPacker, VPacker
alphabet = ['Q', 'W', 'E', 'R', 'T', 'Y', 'U', 'I', 'O', 'P', 'A', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'Z', 'X']
... | {"hexsha": "98b360cdb60bf014483492c62bafca2bbaec9cb1", "size": 5986, "ext": "py", "lang": "Python", "max_stars_repo_path": "annotation_tool/utils/utils.py", "max_stars_repo_name": "NoelShin/PixelPick", "max_stars_repo_head_hexsha": "f0ae7d35f62c1dda70f5bff1689177a513ab6259", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import torch
import math
import numpy as np
from torch.utils.data import Dataset, Subset
from HParams import HParams
class ComboDataset(Dataset):
def __init__(self, hp: HParams, device):
self.hp = hp
folder = hp.sketches_folder
from os import listdir
from os.path import i... | {"hexsha": "be96e0eb52a3469dd183463ca6c9a329cb0d2945", "size": 4494, "ext": "py", "lang": "Python", "max_stars_repo_path": "SketchDataset.py", "max_stars_repo_name": "SauceTheBoss/SketchAAE", "max_stars_repo_head_hexsha": "f6ef9b4143643e2d6ab14299291a5d64fd9c9fef", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
subroutine nrain
!! ~ ~ ~ PURPOSE ~ ~ ~
!! this subroutine adds nitrate from rainfall to the soil profile
!! ~ ~ ~ INCOMING VARIABLES ~ ~ ~
!! name |units |definition
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
!! curyr |none ... | {"hexsha": "7b0a5eb41cce5e92c06af08a58c1a0852d6974fb", "size": 3701, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "swat_cli/rev670_source/nrain.f", "max_stars_repo_name": "GISWAT/erosion-sediment", "max_stars_repo_head_hexsha": "6ab469eba99cba8e5c365cd4d18cba2e8781ccf6", "max_stars_repo_licenses": ["MIT"], "ma... |
from functools import partial
import numpy as np
import pytest
import torch
from sklearn.metrics import precision_recall_curve as _sk_precision_recall_curve
from pytorch_lightning.metrics.classification.precision_recall_curve import PrecisionRecallCurve
from pytorch_lightning.metrics.functional.precision_recall_curve... | {"hexsha": "07e942c5b10f4f56b7114aecb06da2afebafde75", "size": 4030, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/metrics/classification/test_precision_recall_curve.py", "max_stars_repo_name": "nightlessbaron/pytorch-lightning", "max_stars_repo_head_hexsha": "239bea5c29cef0d1a0cfb319de5dbc9227aa2a53", "... |
import xml.etree.ElementTree as et
import re
import pandas as pd
from datetime import datetime
start = datetime.now()
from tqdm.auto import tqdm
import numpy as np
import os
#Please specify your dataset directory.
os.chdir("your dataset directory")
Id=[]
CreationDate=[]
Score=[]
ParentId=[]
Body=[]
CommentCount=[]
... | {"hexsha": "86b69b1d272565e8270842d025c43524185597e4", "size": 1940, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/1. Data collection/2_All_answer_post_SO.py", "max_stars_repo_name": "NAIST-SE/Package_management_system", "max_stars_repo_head_hexsha": "718258be23e4a471d136de9d9b2bc850b7e35068", "max_sta... |
#!/ebio/ag-neher/share/programs/EPD/bin/python
'''
author: Taylor Kessinger & Richard Neher
date: 10/07/2014
content: generate beta coalescent trees and calculate their SFS
'''
import os
import numpy as np
import random as rand
import scipy.special as sf
from Bio import Phylo
from betatree import *
def lo... | {"hexsha": "59a9a519bd3f6b9593a926849abd98fb64d21e51", "size": 6049, "ext": "py", "lang": "Python", "max_stars_repo_path": "jungle/resources/betatree/src/sfs_py3.py", "max_stars_repo_name": "felixhorns/jungle", "max_stars_repo_head_hexsha": "da50104dcdd2427fcaa5ed190f0bd7f2097e2e79", "max_stars_repo_licenses": ["MIT"],... |
#include <path_follower/controller/robotcontroller_ackermann_stanley.h>
#include <path_follower/utils/pose_tracker.h>
#include <ros/ros.h>
#include <path_follower/utils/visualizer.h>
#include <cslibs_navigation_utilities/MathHelper.h>
#include <deque>
#include <limits>
#include <boost/algorithm/clamp.hpp>
#includ... | {"hexsha": "7096581397eb5eb658aba3d78558ea09de96e48a", "size": 3114, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "path_follower/src/controller/robotcontroller_ackermann_stanley.cpp", "max_stars_repo_name": "sunarditay/gerona", "max_stars_repo_head_hexsha": "7ca6bb169571d498c4a2d627faddc8cbe590d2c9", "max_stars_... |
MODULE W1JJG_I
INTERFACE
!
SUBROUTINE W1JJG(K1,QM1,QM2,IK,BK,ID,BD,WW)
USE vast_kind_param, ONLY: DOUBLE
INTEGER, INTENT(IN) :: K1
INTEGER, INTENT(IN), DIMENSION(7) :: IK, ID
REAL(DOUBLE), INTENT(IN) :: QM1, QM2
REAL(DOUBLE), INTENT(... | {"hexsha": "05bddb5b443c240657caae4c3ff122a0f4a1e50f", "size": 457, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/librang90/w1jjg_I.f90", "max_stars_repo_name": "sylas/grasp-continuum", "max_stars_repo_head_hexsha": "f5e2fb18bb2bca4f715072190bf455fba889320f", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma sorted_list_of_set_filter:
assumes "finite A"
shows "sorted_list_of_set ({x\<in>A. P x}) = filter P (sorted_list_of_set A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sorted_list_of_set {x \<in> A. P x} = filter P (sorted_list_of_set A)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
u... | {"llama_tokens": 11488, "file": "Skip_Lists_Misc", "length": 63} |
# <markdowncell> Import scipy.io for matlab loading, numpy and teneto
# <codecell>
import scipy.io as sio
import scipy.stats as sps
import numpy as np
import teneto
import matplotlib.pyplot as plt
import pandas as pd
import os
# <markdowncell> Set matplotlib color style
# <codecell>
plt.rcParams['image.cmap'] = ... | {"hexsha": "e0220c3008e91c60a9dc638d6e470025a53f6935", "size": 3490, "ext": "py", "lang": "Python", "max_stars_repo_path": "paper_iv/centrality_fmri_plot.py", "max_stars_repo_name": "wiheto/phd_code", "max_stars_repo_head_hexsha": "432cae1aa26f1758e5970fd11361af0e4a130b9d", "max_stars_repo_licenses": ["MIT"], "max_star... |
import torch
import networkx as nx
import torch.nn.functional as F
from collections import Counter
from autodesk_colab_KG import load_data
from torch_geometric.data import DataLoader, Data
from sklearn.model_selection import train_test_split
def get_vocab():
graphs = load_data('data/autodesk_colab_fullv3_20201029... | {"hexsha": "33470faa7066b0234984a02026f5a80c69768289", "size": 6258, "ext": "py", "lang": "Python", "max_stars_repo_path": "GNN/dataset.py", "max_stars_repo_name": "danielegrandi-adsk/OSDR-GNN", "max_stars_repo_head_hexsha": "a21c9dd1410ba77867e5c4e1b8b762e2b971ebeb", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Loading wind data
RESData = CSV.read(pwd()*"/data/RESdata.csv", DataFrame)
RESnodes = [3,5,7,16,21,23]
RenG = length(RESnodes)
SolarData = zeros(RenG,24,365)
for i in 1:RenG
global WindData, SolarData
CitySolar = RESData[:,i]
SolarData[i,:,:] = reshape(CitySolar, 24, 36... | {"hexsha": "4ea05d091fd68ef0e2fb064632d1314ed17fed2d", "size": 1887, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/functions/UCdata.jl", "max_stars_repo_name": "gonzalez-alvaro/MultidimensionalGAPM", "max_stars_repo_head_hexsha": "605ca8f13feb189b5932ac72a77504e65bbd02c7", "max_stars_repo_licenses": ["MIT"]... |
from rest_framework.response import Response
from rest_framework.permissions import AllowAny
from rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND
from rest_framework.decorators import api_view, permission_classes, authentication_classes
from datetime import datetime
import traceback
#
import image... | {"hexsha": "6ea3beb313ef795eaa1f23c6974a4f4a32005c3c", "size": 3239, "ext": "py", "lang": "Python", "max_stars_repo_path": "apiApp/views.py", "max_stars_repo_name": "Alex-Bruno/steller-web", "max_stars_repo_head_hexsha": "9dba1766989f9aaf165a26434dda40cf5c5cc409", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
struct ClebschGordan{A,B,C,j₁T,j₂T}
j₁::A
m₁::A
j₂::B
m₂::B
J::C
M::C
end
Base.zero(::Type{ClebschGordan{A,B,C,j₁T,j₂T}}) where {A,B,C,j₁T,j₂T} =
ClebschGordan{A,B,C,j₁T,j₂T}(zero(A),zero(A),zero(B),zero(B),zero(C),zero(C))
const ClebschGordanℓs{I<:Integer} = ClebschGordan{I,Rational{I},Ra... | {"hexsha": "65e650bfa67ec3b592f6f4b0ba23da3cd2c8c726", "size": 5251, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/jj2lsj.jl", "max_stars_repo_name": "mortenpi/AtomicLevels.jl", "max_stars_repo_head_hexsha": "7900a763be3436922b3dc8dce78660e9de8b5817", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.cm import ScalarMappable
from matplotlib.figure import SubplotParams
import os
dpi = 300
ax_pad = 10
label_fontdict = {'size': 7}
title_fontdict = {'size': 10}
cbar_inset = [1.02, 0, .0125, .96]
cbar_titel_fontdict = {'size': 7}
cbar_labels_fontdi... | {"hexsha": "6533560406963ab8168ee2c166599633b7847934", "size": 2605, "ext": "py", "lang": "Python", "max_stars_repo_path": "spatialHeterogeneity/plotting/utils.py", "max_stars_repo_name": "histocartography/athena", "max_stars_repo_head_hexsha": "8a1af389dc936bf5b8e62c56ae682a2fe4d2d71a", "max_stars_repo_licenses": ["BS... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Copyright 2017 Timothy Dozat
#
# 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
# ... | {"hexsha": "da2531e4885dc3b06430f47df2509bc15cbe6cc4", "size": 8988, "ext": "py", "lang": "Python", "max_stars_repo_path": "parser/trigraph_parser_network.py", "max_stars_repo_name": "shtechair/Second_Order_SDP", "max_stars_repo_head_hexsha": "d8e90aef1ff9bade86d602790adf08e37ed4c746", "max_stars_repo_licenses": ["Apac... |
# Connector to the ini file using the factory pattern
# The idea is to create a connector
# for experimental and simulation data
# which is transparent and does not require any user input
# except for the ini file
import os,sys
import configparser
import colorsys
import numpy as np
def from_string(s, s_type='float')... | {"hexsha": "565523453580412647ea68a137668026eedce579", "size": 5261, "ext": "py", "lang": "Python", "max_stars_repo_path": "iniConnector.py", "max_stars_repo_name": "gdurin/mokas", "max_stars_repo_head_hexsha": "57893d0191c988b241dcf5701d4213a3cbcf587a", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": null... |
//
// composed_8.cpp
// ~~~~~~~~~~~~~~
//
// Copyright (c) 2003-2020 Christopher M. Kohlhoff (chris at kohlhoff dot com)
//
// 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)
//
#include <boost/asio/compose.hpp>
#incl... | {"hexsha": "4fb6846568b0d31a31ad933872e47d7fda1deea9", "size": 7623, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "venv/boost_1_73_0/libs/asio/example/cpp11/operations/composed_8.cpp", "max_stars_repo_name": "uosorio/heroku_face", "max_stars_repo_head_hexsha": "7d6465e71dba17a15d8edaef520adb2fcd09d91e", "max_sta... |
import math
import time
import librosa.display
import numpy as np
import os
from os import path
import pandas as pd
from tqdm import tqdm
import common_functions
import apr_constants
from utils import features_functions
# Create a csv file from samples
def extract_features(dataset_path=None, exclude_folders_name={}, ... | {"hexsha": "7ff0739a06d784ae0f7c10d177ac77ccaeb5dc63", "size": 5440, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/utils/featuresToCsv.py", "max_stars_repo_name": "bonurag/audioPatternRecognition", "max_stars_repo_head_hexsha": "36fc961050710ac896ab6dad3315322683361c99", "max_stars_repo_licenses": ["MIT"]... |
# identifies patients with gout and thiazides
import csv
import statsmodels.api as statsmodels
from atcs import *
from icd import is_gout
highrisk_prescription_identified = 0
true_positive = 0
true_negative = 0
false_positive = 0
false_negative = 0
gout_treatment = allopurinol | benzbromaron | colchicin | febuxost... | {"hexsha": "c9db482b1c8a717fe8c5e7ba3811937ee21a773e", "size": 2690, "ext": "py", "lang": "Python", "max_stars_repo_path": "gout_classifier.py", "max_stars_repo_name": "wahram/atc_icd", "max_stars_repo_head_hexsha": "e7b9a095bfd2186e85e7d1a276669b7ccab9d5f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
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