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GeezerVison is showing github.com — web/script.js at master · debuggger/web

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81 lines (61 loc) · 2.49 KB
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script.js

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81 lines (61 loc) · 2.49 KB
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(function(){
var app = angular.module('mysite', ['ngSanitize']);
app.controller('mainCtrl', ['$scope', function($scope){
console.log('main controller');
$scope.activeTab = "homePage.html";
$scope.resumeClicked = function(){
$scope.activeTab = "resume.html";
};
$scope.projectsClicked = function(){
$scope.activeTab = "projects.html";
};
$scope.contactClicked = function(){
$scope.activeTab = "contact.html";
};
}]);
app.controller('homePageCtrl', ['$scope', function($scope){
console.log('homePage controller');
}]);
app.controller('resumeCtrl', ['$scope', function($scope){
console.log('resume controller');
}]);
app.controller('contactCtrl', ['$scope', function($scope){
console.log('resume controller');
}]);
app.controller('projectsCtrl', ['$scope', '$sce', function($scope, $sce){
console.log('contact controller');
$scope.projects = [
{
'title':'Handwritten digit classification',
'desc': $sce.trustAsHtml('\
<p>\
The project is intended to compare the performances of 2 classification algorithms namely, Logistic Regression and Neural Networks. The classification problem being solved is that of handwritten digit recognition, an instance of multi class classification involving classifying digits between 0-9. The training and test data used is from the MNIST data-set. The aim of the project is to find the optimal set of weight values that results in the least misclassification rate.\
<img src="574-p3.png">\
</p>\
')
},
{
'title':'Linear Regression',
'desc': 'This project was intended to predict the relevance of search engine results using Linear Regression'
},
{
'title':'Linear Regression',
'desc': 'This project was intended to predict the relevance of search engine results using Linear Regression'
},
{
'title':'Linear Regression',
'desc': 'This project was intended to predict the relevance of search engine results using Linear Regression'
},
{
'title':'Linear Regression',
'desc': 'This project was intended to predict the relevance of search engine results using Linear Regression'
}];
$scope.tileClicked = function(index){
$scope.activeProject = $scope.projects[index];
$('#myModal').modal({
keyboard: false,
show: true
});
};
}]);
})()
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