{"action":"created","issue":{"url":"https://api.github.com/repos/Aplokodika/MachineLearning/issues/2","repository_url":"https://api.github.com/repos/Aplokodika/MachineLearning","labels_url":"https://api.github.com/repos/Aplokodika/MachineLearning/issues/2/labels{/name}","comments_url":"https://api.github.com/repos/Aplokodika/MachineLearning/issues/2/comments","events_url":"https://api.github.com/repos/Aplokodika/MachineLearning/issues/2/events","html_url":"https://github.com/Aplokodika/MachineLearning/issues/2","id":167227091,"number":2,"title":"Yogesh: write a file-handling code with the following 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Working Neural Network Model","description":"The first milestone of this project will be to create a working neural network model, using which we may write code to do directly implementable projects addressing social cause.","creator":{"login":"sreramk","id":17235911,"avatar_url":"https://avatars.githubusercontent.com/u/17235911?v=3","gravatar_id":"","url":"https://api.github.com/users/sreramk","html_url":"https://github.com/sreramk","followers_url":"https://api.github.com/users/sreramk/followers","following_url":"https://api.github.com/users/sreramk/following{/other_user}","gists_url":"https://api.github.com/users/sreramk/gists{/gist_id}","starred_url":"https://api.github.com/users/sreramk/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sreramk/subscriptions","organizations_url":"https://api.github.com/users/sreramk/orgs","repos_url":"https://api.github.com/users/sreramk/repos","events_url":"https://api.github.com/users/sreramk/events{/privacy}","received_events_url":"https://api.github.com/users/sreramk/received_events","type":"User","site_admin":false},"open_issues":3,"closed_issues":0,"state":"open","created_at":"2016-07-24T10:45:54Z","updated_at":"2016-07-25T13:55:47Z","due_on":null,"closed_at":null},"comments":1,"created_at":"2016-07-24T10:22:36Z","updated_at":"2016-07-25T18:40:10Z","closed_at":null,"body":"**Language(s) to use: Java**\r\n**Deadline                 :31st July 2016**\r\n**Licence to use with : GNU public license v3** \r\n**Task assigned to: @YogeshKumarR .**\r\n\r\n# Implement the following class in Java. \r\n\r\nThe purpose of the following class for permanently storing  \r\n\r\n```\r\n      class WeightDataFile{\r\n\t/*\r\n    * This variable stores the directory to the file which contains the weight values \r\n    * and the directory to which the weight values need to be stored. \r\n    */\r\n    public String readFileName, writeFileName;\r\n    \r\n    /*\r\n     * contains the weight values connecting each of the  neural network link\r\n     */\r\n     public ArrayList<ArrayList<Float>>  weightData; // 2D extendible array.\r\n     \r\n     /*\r\n     * This defines the maximum number of Neurons in each layers. So the number \r\n     * of weight values between any two layers i, i+1 will be, \r\n     * dataSignature[i] * dataSignature[i+1]. Note that i + 1 = number of layers - 1\r\n     * when the index starts from zero.  \r\n     */ \r\n     public ArrayList<Integer> dataSignature;\r\n     \r\n     \r\n     /*\r\n      * Methods: \r\n      */\r\n     \r\n     /* Sets the read-write file names. \r\n      * */\r\n     public void setFileNames(String  readFileN, String  writeFileN){\r\n    \t readFileName = new String(readFileN.toString());\r\n    \t writeFileName  = new String(writeFileN.toString());\r\n     }\r\n     \r\n     public void getDataFromFile(){\r\n    \t/* This method obtains the file header as well as the weight values of each\r\n    \t * neurons and store them in the 2D array.  \r\n    \t * */ \r\n     }\r\n     \r\n     public void putDataToFile(){\r\n    \t  /*\r\n    \t   * This first writes the header data and finally writes the weight values to \r\n    \t   * the file.\r\n    \t   */\r\n      }\r\n     \r\n     public void setDataSignature(ArrayList<Integer>  dataSig){\r\n    \t /*\r\n    \t  * This sets the dataSignature variable\r\n    \t  */\r\n     }\r\n     \r\n     public void readHeader(){\r\n    \t /*\r\n    \t  * this reads the header from the file. \r\n    \t  */\r\n     }\r\n     \r\n     public void generateHeader(){\r\n    \t /*\r\n    \t  * This generates the file header using the information from the variable \r\n    \t  * dataSignature\r\n    \t  */\r\n     }\r\n     \r\n     public void writeHeader(){\r\n    \t /*\r\n    \t  * This writes the header data to the file. \r\n    \t  */\r\n     }\r\n\r\n}\r\n                       \r\n```\r\nThe above class must read weight values from a file and must be able to rewrite those values back to the file. The variable `weightData` is made public to invite external modification. Weight values can them be written to  `weightData` so that it may be serialized and written to a file later. `dataSignature` must also be set. \r\n\r\nThe above description does not contain the structure for the file header which you are free to define yourself. **Upload the respective .Java files and .class files to the repository MachineLearning and place it under the folder `ToBeTested` once completed**. Outside contributors are also accepted, in which case, all efficient codes will be retained.   \r\n\r\n**You may contact me if you have any queries or you may post your query as an issue which will be addressed as soon as possible**  \r\n"},"comment":{"url":"https://api.github.com/repos/Aplokodika/MachineLearning/issues/comments/235044693","html_url":"https://github.com/Aplokodika/MachineLearning/issues/2#issuecomment-235044693","issue_url":"https://api.github.com/repos/Aplokodika/MachineLearning/issues/2","id":235044693,"user":{"login":"sreramk","id":17235911,"avatar_url":"https://avatars.githubusercontent.com/u/17235911?v=3","gravatar_id":"","url":"https://api.github.com/users/sreramk","html_url":"https://github.com/sreramk","followers_url":"https://api.github.com/users/sreramk/followers","following_url":"https://api.github.com/users/sreramk/following{/other_user}","gists_url":"https://api.github.com/users/sreramk/gists{/gist_id}","starred_url":"https://api.github.com/users/sreramk/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sreramk/subscriptions","organizations_url":"https://api.github.com/users/sreramk/orgs","repos_url":"https://api.github.com/users/sreramk/repos","events_url":"https://api.github.com/users/sreramk/events{/privacy}","received_events_url":"https://api.github.com/users/sreramk/received_events","type":"User","site_admin":false},"created_at":"2016-07-25T18:40:10Z","updated_at":"2016-07-25T18:40:10Z","body":"Yogesh, first work on the \r\n```\r\n  public void getDataFromFile(){\r\n        /* This method obtains the file header as well as the weight values of each\r\n         * neurons and store them in the 2D array.  \r\n         * */ \r\n     }\r\n```\r\nmethod. \r\n\r\nFor this, you need a data-structure for storing the data in a file. I'll tell you the requirements and you can create the data structure yourself. \r\n\r\n1. The header should contain two pieces of information. \r\n\r\n2. The first one is the number of layers (let it be `l`).  \r\n\r\n3. The second one is, an array of size `l` containing the number of neurons in each layer; let us denote it by `N[i]`, where `i` varies from `0` to `l - 1`. \r\n4. Total number of individual float values present in the file can be computed as: `N[0] * N[1] * N[2] *... *N[l-1]`\r\n5. Each of these values must be stored in an array. The data-structure for storage is of your choice.  "}}
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