text:
@article{glmnet,
    author    = {Jerome Friedman and
                 Trevor Hastie and
                 Rob Tibshirani},
    title     = {\textbf{Regularization Paths for Generalized Linear Models via Coordinate Descent}},
    journal   = {\textit{Journal of Statistical Software}},
    volume    = {33},
    number    = {1},
    pages     = {},
    year      = {2009},
    url       = {http://core.ac.uk/download/pdf/6287975.pdf}
}

@article{strongrules,
    author    = {Jacob Bien and
		 Jerome Friedman and
                 Trevor Hastie and
		 Noah Simon and
		 Jonathan Taylor and
                 Rob Tibshirani and
		 Ryan J. Tibshirani},
    title     = {\textbf{Strong Rules for Discarding Predictors in Lasso-type Problems}},
    journal   = {\textit{Journal of the Royal Statistical Society. Series B}},
    volume    = {74},
    number    = {1},
    pages     = {},
    year      = {2012},
    url       = {http://statweb.stanford.edu/~tibs/ftp/strong.pdf}
}

@ARTICLE{elastic,
    author = {Hui Zou and Trevor Hastie},
    title = {\textbf{Regularization and variable selection via the Elastic Net}},
    journal = {\textit{Journal of the Royal Statistical Society. Series B}},
    year = {2005},
    volume = {67},
    pages = {301--320}
}

@article{lasso,
    author    = {Rob Tibshirani},
    title     = {\textbf{Regression Shrinkage and Selection via the Lasso}},
    journal   = {\textit{Journal of the Royal Statistical Society. Series B (Methodological)}},
    volume    = {58},
    number    = {1},
    pages     = {267-288},
    year      = {1996},
    url       = {http://statweb.stanford.edu/~tibs/lasso/lasso.pdf}
}

@article{admm,
    author    = {Stephen Boyd and
		 Neal Parikh and
		 Eric Chu and
		 Borja Peleato and
		 Jonahtan Eckstein},
    title     = {\textbf{Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers}},
    journal   = {\textit{Foundations and Trends in Machine Learning}},
    volume    = {3},
    number    = {1},
    pages     = {1-122},
    year      = {1996},
    url       = {https://web.stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf}
}

@misc{cliffgbm,
    author    = {Cliff Click},
    title     = {\textbf{Building a Distributed GBM on H2O}},
    journal   = {{H2O.ai Blog}},
    year      = {2013},
    url       = {{http://h2o.ai/blog/2013/10/building-distributed-gbm-h2o/}}
}

@article{bias,
    author    = {Thomas Dietterich and
		 Eun Bae Kong},
    title     = {\textbf{Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms}},
    year      = {1995},
    url       = {http://www.iiia.csic.es/~vtorra/tr-bias.pdf},
}

@article{boost,
    author    = {Jane Elith and
                 John R. Leathwick and
                 Trevor Hastie},
    title     = {\textbf{A Working Guide to Boosted Regression Trees}},
    journal   = {\textit{Journal of Animal Ecology}},
    volume    = {77},
    number    = {4},
    pages     = {802-813},
    year      = {2008},
    url       = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2008.01390.x/abstract}
}

@article{greedyfunction,
    author = {Jerome H. Friedman},
    title = {\textbf{Greedy Function Approximation: A Gradient Boosting Machine}},
    journal = {\textit{Annals of Statistics}},
    year = {1999},
    volume = {29},
    pages = {1189--1232},
    url   = {http://statweb.stanford.edu/?jhf/ftp/trebst.pdf}
}

@article{prox,
    author    = {Neal Parikh and
                 Stephen Boyd},
    title     = {\textbf{Proximal Algorithms}},
    journal   = {\textit{Foundations and Trends in Optimization}},
    volume    = {1},
    number    = {3},
    pages     = {123-231},
    year      = {2014},
    url       = {http://web.stanford.edu/~boyd/papers/pdf/prox_algs.pdf}
}

@article{discussion,
    author    = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    title     = {\textbf{Discussion of Boosting Papers}},
    journal   = {\textit{Annual Statistics}},
    volume    = {32},
    pages     = {102-107},
    year      = {2004},
    url       = {http://web.stanford.edu/?hastie/Papers/boost_discussion.pdf}
}

@article{additivelogistic,
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    title = {\textbf{Additive Logistic Regression: a Statistical View of Boosting}},
    journal = {\textit{Annals of Statistics}},
    year = {1998},
    volume = {28},
    pages = {2000},
    url = {http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle= euclid.aos/1016218223}
}

@book{esl,
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    title = {\textbf{The Elements of Statistical Learning}},
    booktitle = {\textit{The Elements of Statistical Learning}},
    publisher = {Springer},
    address = {New York},
    year = {2001},
    url = {http://statweb.stanford.edu/?tibs/ElemStatLearn/printings/ESLII_print10.pdf}
}

@misc{performance,
    author = {Candel, Arno},
    title = {\textbf{Definitive Performance Tuning Guide for Deep Learning}},
    journal = {H2O.ai Blog},
    type = {Blog},
    number = {February 26},
    year = {2015},
    howpublished = {\url{http://h2o.ai/blog/2015/02/deep-learning-performance/}}
}

@article{neuralnet,
    author = {G.E. Hinton and R.R. Salakhutdinov},
    title = {\textbf{Reducing the Dimensionality of Data with Neural Networks}},
    journal = {\textit{Science}},
    volume = {313},
    year = {2006},
    pages = {504--507},
    url = {http://www.cs.toronto.edu/~hinton/science.pdf}
}

@online{mnist,
	author = {LeCun, Yann and Cortes, Corinna and Burgres, Christopher J.C},
	title = {\textbf{The MNIST Database}},
	url = {http://yann.lecun.com/exdb/mnist/}
}

@online{h2ogithubdl,
        author = {H2O.ai Team},
        title = {\textbf{H2O GitHub repository for Deep Learning documentation}},
        url = {https://github.com/h2oai/h2o/tree/master/docs/deeplearning/DeepLearningRVignetteDemo},
	year = {\the\year}
}

@misc{h2osite,
        author = {H2O.ai Team},
        title = {\textbf{H2O website}},
        journal = {H2O.ai},
	url = {http://h2o.ai},
	year = {\the\year}
}

@misc{h2odocs,
        author = {H2O.ai Team},
        title = {\textbf{H2O documentation}},
        url = {http://docs.h2o.ai},
	year = {\the\year}
}

@misc{h2ogithubrepo,
        author = {H2O.ai Team},
        title = {\textbf{H2O GitHub Repository}},
        url = {https://github.com/h2oai},
	year = {\the\year}
}

@misc{h2odatasets,
        author = {H2O.ai Team},
        title = {\textbf{H2O Datasets}},
        url = {http://data.h2o.ai},
	year = {\the\year}
}

@misc{h2ojira,
        author = {H2O.ai Team},
        title = {\textbf{H2O JIRA}},
        url = {https://jira.h2o.ai},
	year = {\the\year}
}

@misc{dlarchitecture,
    author = {Yoshua Bengio},
    title = {\textbf{Learning Deep Architectures for AI}},
    year = {2009},
    url = {http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf}
}

@misc{efficientbackprop,
    author = {Yann Lecun and Leon Bottou and Genevieve B. Orr and Klaus-Robert Müller},
    title = {\textbf{Efficient BackProp}},
    year = {1998},
    url = {http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf}
}

@INPROCEEDINGS{maxoutnetworks,
    author = {Ian J. Goodfellow and David Warde-farley and Mehdi Mirza and Aaron Courville and Yoshua Bengio},
    title = {\textbf{Maxout networks}},
    booktitle = {\textit{In ICML}},
    year = {2013},
    url = {http://arxiv.org/pdf/1302.4389.pdf}
}

@INPROCEEDINGS{hogwild,
    author = {Feng Niu and Benjamin Recht and Christopher Ré and Stephen J. Wright},
    title = {\textbf{Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent}},
    booktitle = {In NIPS},
    year = {2011}
}

@inproceedings{adadelta,
    author = {Matthew D. Zeiler},
    title = {\textbf{ADADELTA: An Adaptive Learning Rate Method}},
    year = {2012},
    url = {http://arxiv.org/pdf/1212.5701v1.pdf}
}

@article{momentum,
    author = {Ilya Sutskever and James Martens and George Dahl and Geoffrey Hinton},
    title = {\textbf{On the importance of initialization and momentum in deep learning}},
    year = {2014},
    url = {http://www.cs.toronto.edu/~fritz/absps/momentum.pdf}
}

@article{featuredetectors,
    author = {G. E. Hinton and N. Srivastava and A. Krizhevsky and I. Sutskever and R. R. Salakhutdinov},
    title = {\textbf{Improving neural networks by preventing co-adaptation of feature detectors}},
    year = {2012},
    url = {http://arxiv.org/pdf/1207.0580.pdf}
}

@online{rdocs,
        author = {H2O.ai Team},
        title = {\textbf{H2O R Package Documentation}},
        url = {http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Rdoc.html},
	year = {\the\year}
}

@Manual{h2o_GLM_booklet,
    title = {\textbf{Generalized Linear Modeling with H2O}},
    author = {Hussami, N. and Kraljevic, T. and Lanford, J. and Nykodym, T. and Rao, A. and Wang, A.},
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_DL_booklet,
    title = {\textbf{Deep Learning with H2O}},
    author = {Arora, A. and Candel, A. and Lanford, J. and LeDell, E. and and Parmar, V.},
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_GBM_booklet,
    title = {\textbf{Gradient Boosted Models}},
    author = {Click, C. and Lanford, J. and Malohlava, M. and Parmar, V. and Roark, H.},
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_R_booklet,
    title = {\textbf{Fast Scalable R with H2O}},
    author = {Aiello, S. and Eckstrand, E. and Fu, A. and Landry, M. and Lanford, J. and Aboyoun, P. },
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_R_package,
    title = {\textbf{h2o: R Interface for H2O}},
    author = {H2O.ai Team},
    year = {\the\year},
    note = {R package version 3.1.0.99999},
    url = {http://www.h2o.ai},
}

@Manual{h2o_Python_module,
    title = {\textbf{h2o: Python Interface for H2O}},
    author = {H2O.ai Team},
    year = {\the\year},
    note = {Python package version 3.1.0.99999},
    url = {http://www.h2o.ai},
}

@Manual{h2o_Java_software,
    title = {\textbf{H2O: Scalable Machine Learning}},
    author = {H2O.ai Team},
    year = {\the\year},
    note = {version 3.1.0.99999},
    url = {http://www.h2o.ai},
}

@misc{stream,
        author = {H2O.ai Team},
        title = {\textbf{H2Ostream}},
        url = {https://groups.google.com/d/forum/h2ostream},
	year = {\the\year}
}

@misc{rensemble,
	author = {Erin LeDell},
	title = {\textbf{R Ensemble Documentation}},
	url = {http://www.stat.berkeley.edu/~ledell/R/h2oEnsemble.pdf},
	year = {2014}
}

@Manual{r,
  title        = {R: A Language and Environment for Statistical
                  Computing},
  author       = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address      = {Vienna, Austria},
  year         = 2015,
  url          = {https://www.R-project.org}
}

@misc{Pydocs,
	author = {H2O.ai Team},
	title = {\textbf{H2O Python Documentation}},
	url = {http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Pydoc.html},
	year = {2015}
}

@inproceedings{lenet,
    author = {Yann LeCun and Bottou, Leon and Bengio, Yoshua and Haffner, Patrick},
    title = {\textbf{Gradient-Based Learning Applied to Document Recognition}},
    booktitle = {\textit{Proceedings of the IEEE}},
    year = {Nov. 1998},
    url = {http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf}
}

@incollection{alexnet,
	author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
	title = {\textbf{ImageNet Classification with Deep Convolutional Neural Networks}},
	booktitle = {Advances in Neural Information Processing Systems 25},
	editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
	pages = {1097--1105},
	year = {2012},
	url = {https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
}

@article{vgg,
    author = {Simonyan, Karen and Zisserman, Andrew},
    title = {\textbf{Very Deep Convolutional Networks for Large-Scale Image Recognition}},
    year = {2014},
    url = {http://arxiv.org/pdf/1207.0580.pdf}
}

@article{googlenet,
    author = {Christian Szegedy and
               Wei Liu and
               Yangqing Jia and
               Pierre Sermanet and
               Scott E. Reed and
               Dragomir Anguelov and
               Dumitru Erhan and
               Vincent Vanhoucke and
               Andrew Rabinovich},
    title = {\textbf{Going Deeper with Convolutions}},
    year = {2014},
    url = {https://arxiv.org/pdf/1409.4842.pdf}
}

@article{inceptionbn,
    author = {Sergey Ioffe and Christian Szegedy},
    title = {\textbf{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}},
    year = {2015},
    url = {https://arxiv.org/pdf/1502.03167.pdf}
}

@article{resnet,
    author = {Kaiming He and
							 Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
    title = {\textbf{Deep Residual Learning for Image Recognition}},
    year = {2015},
    url = {https://arxiv.org/pdf/1512.03385.pdf}
}

@article{bergstra-bengio,
 author = {Bergstra, James and Bengio, Yoshua},
 title = {\textbf{Random Search for Hyper-parameter Optimization}},
 journal = {J. Mach. Learn. Res.},
 issue_date = {3/1/2012},
 volume = {13},
 month = Feb,
 year = {2012},
 issn = {1532-4435},
 pages = {281--305},
 numpages = {25},
 url = {http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf},
 publisher = {JMLR.org}
}


text:
package testutils

import (
	"net/http"
	"net/http/httptest"
	"testing"

	"github.com/stretchr/testify/assert"
)

func TestGetMockedConstant(t *testing.T) {
	response := httptest.NewRecorder()
	request, err := http.NewRequest(http.MethodGet, "http://localhost:8080/something", nil)
	assert.Nil(t, err)
	request.Header = http.Header{"X-Mock": {"true"}}
	c, _ := GetMockedContext(request, response)

	assert.EqualValues(t, http.MethodGet, c.Request.Method)
	assert.EqualValues(t, "8080", c.Request.URL.Port())
	assert.EqualValues(t, "/something", c.Request.URL.Path)
	assert.EqualValues(t, "http", c.Request.URL.Scheme)
	assert.EqualValues(t, 1, len(c.Request.Header))
	assert.EqualValues(t, "true", c.GetHeader("x-mock"))
	assert.EqualValues(t, "true", c.GetHeader("X-mock"))

}

func TestGetMockedConstantParams(t *testing.T) {
	response := httptest.NewRecorder()
	request, err := http.NewRequest(http.MethodGet, "http://localhost:8080/something", nil)
	assert.Nil(t, err)
	request.Header = http.Header{"X-Mock": {"true"}}

	params := map[string]string{"owner": "myowner", "repo": "myrepo", "state": "all"}

	c, _ := GetMockedContextWithParams(request, response, params)

	assert.EqualValues(t, http.MethodGet, c.Request.Method)
	assert.EqualValues(t, "8080", c.Request.URL.Port())
	assert.EqualValues(t, "/something", c.Request.URL.Path)
	assert.EqualValues(t, "http", c.Request.URL.Scheme)
	assert.EqualValues(t, 1, len(c.Request.Header))
	assert.EqualValues(t, "true", c.GetHeader("x-mock"))
	assert.EqualValues(t, "true", c.GetHeader("X-mock"))
	assert.EqualValues(t, "myowner", c.Param("owner"))
	assert.EqualValues(t, "myrepo", c.Param("repo"))
	assert.EqualValues(t, "all", c.Param("state"))

}

license: MIT

repository: greendinosaur/gh-commit-info

license_type: permissive

repo_name: greendinosaur/gh-commit-info

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: Go

extension: go


text:
package dll

import (
	"testing"
)

func TestFunctions(t *testing.T) {
	GetvJoyVersion()
	VJoyEnabled()
	GetvJoyProductString()
	GetvJoyManufacturerString()
	GetvJoySerialNumberString()
	rid := uint(1)
	GetVJDButtonNumber(rid)
	GetVJDContPovNumber(rid)
	GetVJDAxisExist(rid, AXIS_X)
	var int32val int32
	GetVJDAxisMax(rid, AXIS_X, &int32val)
	GetVJDAxisMin(rid, AXIS_X, &int32val)
	GetVJDStatus(rid)
	ResetAll()
	if AcquireVJD(rid) {
		t.Log("vjd acquired")
		ResetVJD(rid)
		ResetButtons(rid)
		ResetPovs(rid)
		SetAxis(30000, rid, AXIS_X)
		SetBtn(true, rid, 1)
		SetDiscPov(5, rid, 1)
		SetContPov(5, rid, 1)
		RelinquishVJD(rid)
	} else {
		t.Log("no vjd")
	}
}


text:
@article{4351913,
author={M. E. Sargin and Y. Yemez and E. Erzin and A. M. Tekalp},
journal={IEEE Transactions on Multimedia},
title={Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis},
year={2007},
volume={9},
number={7},
pages={1396-1403},
keywords={feature extraction;sensor fusion;speaker recognition;synchronisation;audiovisual synchronization;canonical correlation analysis;data fusion;multimodal fusion strategy;open-set speaker identification;lip texture features;speech features;equal error rates;Speech analysis;Speech recognition;Mel frequency cepstral coefficient;Independent component analysis;Linear discriminant analysis;Discrete cosine transforms;Mutual information;Error analysis;Speaker recognition;Shape;Audiovisual synchronization;correlation;multimodal fusion;speaker recognition},
doi={10.1109/TMM.2007.906583},
ISSN={1520-9210},
month={Nov},}

repository: ahmetuysal/KU-LaTeX-Beamer-Template

repo_name: ahmetuysal/KU-LaTeX-Beamer-Template

extension: bib


text:
@inproceedings{RN394,
 author = {Wangberg, A. and Engelke, N. and Karakok, G.},
 booktitle = {Fourteenth Annual Research in Undergraduate Mathematics Education Conference},
 title = {Function Composition and the Chain Rule in Calculus},
 type = {Conference Proceedings}
}


text:
@article{Nan2019c,
 abstract = {Magnetization dynamics induced by spin–orbit torques in a heavy-metal/ferromagnet can potentially be used to design low-power spintronics and logic devices. Recent computations have suggested that a strain-mediated spin–orbit torque (SOT) switching in magnetoelectric (ME) heterostructures is fast, energy-efficient, and permits a deterministic 180° magnetization switching. However, its experimental realization has remained elusive. Here, the coexistence of the strain-mediated ME coupling and the SOT in a CoFeB/Pt/ferroelectric hybrid structure is shown experimentally. The voltage-induced strain only slightly modifies the efficiency of SOT generation, but it gives rise to an effective magnetic anisotropy and rotates the magnetic easy axis which eliminates the incubation delay in current-induced magnetization switching. The phase field simulations show that the electric-field-induced effective magnetic anisotropy field can reduce the switching time approximately by a factor of three for SOT in-plane magnetization switching. It is anticipated that such strain-mediated ME-SOT hybrid structures may enable field-free, ultrafast magnetization switching.},
 author = {Nan, Tianxiang and Hu, Jia Mian and Dai, Minyi and Emori, Satoru and Wang, Xinjun and Hu, Zhongqiang and Matyushov, Alexei and Chen, Long Qing and Sun, Nian},
 doi = {10.1002/adfm.201806371},
 issn = {16163028},
 journal = {Adv. Funct. Mater.},
 keywords = {magnetoelectric effect,phase-field simulation,spin Hall effect,spin–orbit torque},
 number = {6},
 pages = {1806371},
 title = {A Strain-Mediated Magnetoelectric-Spin-Torque Hybrid Structure},
 volume = {29},
 year = {2019}
}


text:
/* Program generuje liczby losowe do grafu z zadanego zakresu
 * i zapisuje te liczby w pliku
 */

#include <iostream>
#include <string>
#include <fstream>
#include <cstdlib>
#include <cmath>

using namespace std;

int main() {
  int wierzcholki;                    //zakres generowanych liczb
  int liczba_losowa;
  float liczba, gestosc;

  string nazwa;
  //nazwa pliku do zapisu
  string NazwaPliku[100]= {
    "1.dat",
    "2.dat",
    "3.dat",
    "4.dat",
    "5.dat",
    "6.dat",
    "7.dat",
    "8.dat",
    "9.dat",
    "10.dat",
    "11.dat",
    "12.dat",
    "13.dat",
    "14.dat",
    "15.dat",
    "16.dat",
    "17.dat",
    "18.dat",
    "19.dat",
    "20.dat",
    "21.dat",
    "22.dat",
    "23.dat",
    "24.dat",
    "25.dat",
    "26.dat",
    "27.dat",
    "28.dat",
    "29.dat",
    "30.dat",
    "31.dat",
    "32.dat",
    "33.dat",
    "34.dat",
    "35.dat",
    "36.dat",
    "37.dat",
    "38.dat",
    "39.dat",
    "40.dat",
    "41.dat",
    "42.dat",
    "43.dat",
    "44.dat",
    "45.dat",
    "46.dat",
    "47.dat",
    "48.dat",
    "49.dat",
    "50.dat",
    "51.dat",
    "52.dat",
    "53.dat",
    "54.dat",
    "55.dat",
    "56.dat",
    "57.dat",
    "58.dat",
    "59.dat",
    "60.dat",
    "61.dat",
    "62.dat",
    "63.dat",
    "64.dat",
    "65.dat",
    "66.dat",
    "67.dat",
    "68.dat",
    "69.dat",
    "70.dat",
    "71.dat",
    "72.dat",
    "73.dat",
    "74.dat",
    "75.dat",
    "76.dat",
    "77.dat",
    "78.dat",
    "79.dat",
    "80.dat",
    "81.dat",
    "82.dat",
    "83.dat",
    "84.dat",
    "85.dat",
    "86.dat",
    "87.dat",
    "88.dat",
    "89.dat",
    "90.dat",
    "91.dat",
    "92.dat",
    "93.dat",
    "94.dat",
    "95.dat",
    "96.dat",
    "97.dat",
    "98.dat",
    "99.dat",
    "100.dat"
  };

  ofstream PlikWyj;                  //deklaracja pliku do zapisu
  cout << "Podaj liczbe wierzcholkow: ";
  cin >> wierzcholki;
  cout << "Podaj gestosc grafu: ";
  cin >> gestosc;

  liczba = wierzcholki * (wierzcholki-1);
  liczba = liczba * gestosc;
  liczba *= 0.5;
  int krawedzie = floor( liczba );

  for ( int j=0; j < 100; ++j ) {
    nazwa = NazwaPliku[j];

    PlikWyj.open( nazwa, fstream::out );       //otwórz strumień do zapisu

    PlikWyj << wierzcholki;
    PlikWyj << endl;

    PlikWyj << krawedzie;
    PlikWyj << endl;
    PlikWyj << endl;

    for ( int i = 0 ; i < krawedzie; ++i ) {
      do {
	liczba_losowa = rand() % krawedzie;      //wyznaczenie liczby losowej
      }while(liczba_losowa == 0);
      PlikWyj << liczba_losowa << endl;
    }
    if(PlikWyj.fail() )
      {
	PlikWyj.close(); //jeśli udało się zapisać, zamknij strumień
	cout << "Błąd. Nieudało się zapisać";
	return 1;
      }
    else {
      PlikWyj.close();
    }
  }
}

extension: cpp


text:
package net

import (
	"net/http"
)

//go:generate mockery -name HttpDoer

type HttpDoer interface {
	Do(request *http.Request) (*http.Response, error)
}

repository: waives/surf

repo_name: waives/surf