I have multiple datasets here that i took from Kaggle. There are multiple csv files and each csv file is made specifically for sit, stand, walking, running etc. The data is taken from sensors like accelerometers and gyroscopes. The values in datasets are of axis like x, y and z.
Sample Data
Here is a sample dataset of jogging. Now i need to make classifiers in my program so that my program can detect itself whether the data is of jogging, sitting, standing etc. I want to mix all the datasets in a single csv file and then upload it into my webapge and then i want the javascript code to start detecting whether a particular row is of sitting, standing, jogging etc. I don't want any code help but instead i just need a little explanation or a way to start coding it. How can i started making such classifier? I know it is kind of broad question but i think i have tried to explain myself in best way possible. Once my program has detected every row with specific activity it will count all the activities separately and then show it in a table format in webpage.
In order to answer properly to your question, it would be very helpful to know which is your level of understanding and experience with machine learning.
If you are a beginner I would suggest to try to run and understand a couple of tutorials that can be easily found on the web.
If you need an idea of which is the "standard" approach for machine learning development, I will try to give you a general idea of the process.
You can summarize the process in these main steps:
Data pre-processing-> Data splitting -> Feature selection -> Model Training -> Validation -> Deployment
Data pre-processing is meant to clean and format the data: removing NA values, decision about categorical variables, outlier analysis,.... This is a complex step that depends on the application. In your case I would start checking that the data in the different data-sets are homogeneous, i.e. the features have the same meaning across csv and corresponding features respect the same distribution. While the meaning of each feature should be explained in the description of your csv, the check of the distributions could be easily done plotting the box-plots for each feature and csv. If distributions of the same feature across different csv files don't overlap you should investigate further the issue.
An important step in the design of a good model is the splitting of the data. You should split your data in training/validation set (training/validation/test for a more comprehensive approach). This step allows you to train your model on the training set and test the model on the validation set computing unbiased performance of your model. I suggest here to become familiar with concepts as: Cross Validation, stratified-cross-validation, nested-cross-validation for hyper-parameter tuning, overfitting, bias,.... The Validation of the model will give you an idea of the expected performance that it will have on unseen data. If you are considering the use of more than one model, you can use the validation results to choose the "best" one. I suggest here a comparison using the confidence interval or if possible a significance test (e.g t-test, anova,...). Before the deployment the model is trained on all the available data.
The choice of the model depends on the data that you are using: number of samples, number of features, type of variable (numerical, categorical),....
I'm not an expert of javascript, but I believe (just a feeling) that python and R are more common choices for developing Machine learning applications. Both have libraries specifically developed for the task and you can find a lot of materials and tutorial around.
With a bit of more context I think that I could be more specific.
I hope it helps
Related
Our goal is to make sure our users can edit algorithms, see history & also give certain users the right to only see the algorithms.
We have a Workflow engine/API. Every workflow has an instance which has Variables. these can contains values with basic and complex types; Json, String, Int, Guid, DateTime etc.
Certain Steps in the workflow require calculations. these calculations can and should be shared in different workflows.
What I am looking for is something to handle the interface for the users that make the algorithms.
It would need to be able to accept a list of variables with values as input.
(do calculations which users have built algorithms for)
As output a list of Key,Value to be able to save the data in our DB
We could make our own interface I'm just hoping there might be something to save some time. I would prefer to use a tool. Is there anything out there I might not be looking good enough
PS:
I've also looked at the possibility of using Excel as the interface/ saving method. saving the excel as different versions allowing to upload an excel to a calculation (creating a history record.) This would work however there might be tools better suited than excel.
I am an amateur programmer and I want to make a mulitlingual website. My questions are: (For my purposes let the English website be website nr 1 and the Polish nr 2)
Should it be en.example.com and pl.example.com or maybe example.com/en and example.com/pl?
Should I make the full website in one language and then translate it?
How to translate it? Using XML or what? Should website 1 and website 2 be different html files or is there a way to translate a html file and then show the translation using XML or something?
If You need any code or something tell me. Thank You in advance :)
1) I don't think it makes much difference. The most important thing is to ensure that Google can crawl both languages, so don't rely on JavaScript to switch between languages, have everything done server side so both languages can be crawled and ranked in Google.
2) You can do one translation then the other, you just have to ensure that the layout expands to accommodate more/less text without breaking it. Maybe use lorem ipsum whilst designing.
3) I would put the translations into a database and then call that particular translation depending on whether it is EN or PL in the domain name. Ensure that the webpage and database are UTF-8 encoding otherwise you will find that you get 'funny' characters being displayed.
My Advice is that you start to use any Framework.
For instance if you use CakePHP then you have to write
__('My name is')
and in translate file
msgid "My name is"
msgstr "Nazywam się"
Then you can easy translate to any other language and its pretty easy to implement.
Also if you do not want to use Framework you can check this link to see example how it works:
http://tympanus.net/codrops/2009/12/30/easy-php-site-translation/
While this question probably is not a good SO question due to its broad nature. It might be relevant to many users.
My approach would be templates.
Your suggestion of having two html files is bad for the obvious reason of duplication- say you need to change something in your site. You would always need to change two html files- bad.
Having one html file and then parsing it and translating it sounds like a massive headache.
Some templating framework could help you massively. I have been using Smarty, but that's a personal choice and there are many options here.
The idea is you make a template file for your html and instead of actual content you use labels. Then in your php code you include the correct language depending on cookies, user settings or session data.
Storing labels is another issue here. Storing them in a database is a good option, however, remember you do not wish to make 100's of queries against a database for fetching each label. What you can do is store them in a database and then have it generate a language file- an array of labels->translations for faster access and regenerate these files whenever you add/update labels.
Or you can skip the database altogether and just store them in files, however, as these grow they might not be as easy to maintain.
I think the easiest mistake for an "amateur programmer" to make in this area is to allow the two (or more) language versions of the site to diverge. (I've seen so-called professionals make that mistake too...) You need to design it so everything that's common between the two versions is shared, so that when the time comes to make changes, you only need to make the changes in one place. The way you do this will depend on your choice of tools, and I'm not going to start advising on that, because it depends on many factors, for example the extent to which your content is database-oriented.
Closely related to this are questions of who is doing the translation, how the technical developers and the translators work together, and how to keep track of which content needs to be re-translated when it changes. These are essentially process questions rather than coding questions, so not a good fit for SO.
Don't expect that a site for one language can be translated without any technical impact; you will find you have made all sorts of assumptions about the length of strings, the order of fields, about character coding and fonts, and about cultural things like postcodes, that turn out to be invalid when you try to convert the site to a different language.
You could make 2 different language files and use php constants to define the text you want to translate for example:
lang_pl.php:
define("_TEST", "polish translation");
lang_en.php:
define("_TEST", "English translation");
now you could make a choice for the user between polish or english translation and based on that you can include the language file.
So if you would have a text field you put its value to _TEST (inbetween php brackets).
and it would show the translation of the chosen option.
The place i worked was doing it like this: they didn't have to much writing on their site, so they were keeping it in a database. As your tags have a php , I assume you know how to use databases. They had a mysql table called languages with language id(in your case 1 for en and 2 for pl) and texts in columns. So the column names were main heading, intro_text, about_us... when the user comes it selects the language and a php request get the page with that language. This is easy because your content is static and can be translated before site gets online, if your content is dynamic(users add content) you may need to use a translation machine because you cannot expect your users to write their entry in all languages.
I need to develop natural language querying tool for a structured database. I tried two approaches.
using Python nltk (Natural Language Toolkit for python) using
Javascript and JSON (for data source)
In the first case I did some NLP steps to format the natural query by doing removing stop words, stemming, finally mapping keywords using featured grammar mapping. This methodology works for simple scenarios.
Then I moved to second approach. Finding the data in JSON and getting corresponding column name and table name , then building a sql query. For this one, I also implemented removing stop words, stemming using javascript.
Both of these techniques have limitations.I want to implement semantic search approach.
Please can anyone suggest me better approach to do this..
Semantic parsing for NLIDB (natural language interface to data bases) is a very evolved domain with many techniques: rule based methods (involving grammars) or machine learning techniques. They cover a large range of query inputs, and offer much more results than pure NL processing or regex methods.
The technique I favor is based on Feature based context-free grammars FCFG. For starters, in the NTLK book available online, look for the string "sql0.fcfg". The code example shows how to map the NL phrase structure query "What cities are located in China" into an SQL query "SELECT City FROM city_table WHERE Country="china" via the feature "SEM" or semantics of the FCFG.
I recommend Covington's books
NLP for Prloog Programmers (1994)
Prolog Programming in Depth (1997)
They will help You go a long way. These PDF's are downloadable from his site.
As I commented, I think you should add some code, since not everyone has read the book.
Anyway my conclusion is that yes, as you said it has a lot of limitations and the only way to achieve more complex queries is to write very extensive and complete grammar productions, a pretty hard work.
I am new to that area, so the question may seem strange. However before asking I've read bunch of introductory articles about what are the key points about in machine learning and what are the acting parts of neural networks. Including very useful that one What is machine learning. Basically as I got it - an educated NN is (correct me if it's wrong):
set of connections between neurons (maybe self-connected, may have gates, etc.)
formed activation probabilities on each connection.
Both things are adjusted during the training to fit expected output as close as possible. Then, what we do with an educated NN - we load the test subset of data into it and check how good it performs. But what happens if we're happy with the test results and we want to store the education results and not run training again later when dataset get new values.
So my question is - is that education knowledge is stored somewhere except RAM? can be dumped (think of object serialisation in a way) so that you don't need to educate your NN with data you get tomorrow or later.
Now I am trying to make simple demo with my dataset using synaptic.js but I could not spot that kind of concept of saving education in project's wiki.
That library is just an example, if you reference some python lib would be good to!
With regards to storing it via synaptic.js:
This is quite easy to do! It actually has a built-in function for this. There are two ways to do this.
If you want to use the network without training it again
This will create a standalone function of your network, you can use it anywhere with javascript without requiring synaptic.js! Wiki
var standalone = myNetwork.standalone();
If you want to modify the network later on
Just convert your network to a JSON. This can be loaded up anytime again with synaptic.js! Wiki
// Export the network to a JSON which you can save as plain text
var exported = myNetwork.toJSON();
// Conver the network back to useable network
var imported = Network.fromJSON(exported);
I will assume in my answer that you are working with a simple multi-layer perceptron (MLP), although my answer is applicable to other networks too.
The purpose of 'training' an MLP is to find the correct synaptic weights that minimise the error on the network output.
When a neuron is connected to another neuron, its input is given a weight. The neuron performs a function, such as the weighted sum of all inputs, and then outputs the result.
Once you have trained your network, and found these weights, you can verify the results using a validation set.
If you are happy that your network is performing well, you simply record the weights that you applied to each connection. You can store these weights wherever you like (along with a description of the network structure) and then retrieve them later. There is no need to re-train the network every time you would like to use it.
Hope this helps.
I’m trying to show a list of lunch venues around the office with their today’s menus. But the problem is the websites that offer the lunch menus, don’t always offer the same kind of content.
For instance, some of the websites offer a nice JSON output. Look at this one, it offers the English/Finnish course names separately and everything I need is available. There are couple of others like this.
But others, don’t always have a nice output. Like this one. The content is laid out in plain HTML and English and Finnish food names are not exactly ordered. Also food properties like (L, VL, VS, G, etc) are just normal text like the food name.
What, in your opinion, is the best way to scrape all these available data in different formats and turn them into usable data? I tried to make a scraper with Node.js (& phantomjs, etc) but it only works with one website, and it’s not that accurate in case of the food names.
Thanks in advance.
You may use something like kimonolabs.com, they are much easier to use and they give you APIs to update your side.
Remember that they are best for tabular data contents.
There my be simple algorithmic solutions to the problem, If there is a list of all available food names this can be really helpful, you find the occurrence of a food name inside a document (for today).
If there is not any food list, You may use TF/IDF. TF/IDF allows to calculate the score of a word inside a document among the current document and also other documents. But this solution needs enough data to work.
I think the best solution is some thing like this:
Creating a list of all available websites that should be scrapped.
Writing driver classes for each website data.
Each driver has the duty of creating the general domain entity from its standard document.
If you can use PHP, Simple HTML Dom Parser along with Guzzle would be a great choice. These two will provide a jQuery like path finder and a nice wrapper arround HTTP.
You are touching really difficult problem. Unfortunately there are no easy solutions.
Actually there are two different parts to solve:
data scraping from different sources
data integration
Let's start with first problem - data scraping from different sources. In my projects I usually process data in several steps. I have dedicated scrapers for all specific sites I want, and process them in the following order:
fetch raw page (unstructured data)
extract data from page (unstructured data)
extract, convert and map data into page-specific model (fully structured data)
map data from fully structured model to common/normalized model
Steps 1-2 are scraping oriented and steps 3-4 are strictly data-extraction / data-integration oriented.
While you can easily implement steps 1-2 relatively easy using your own webscrapers or by utilizing existing web services - data integration is the most difficult part in your case. You will probably require some machine-learning techniques (shallow, domain specific Natural Language Processing) along with custom heuristics.
In case of such a messy input like this one I would process lines separately and use some dictionary to get rid Finnish/English words and analyse what has left. But in this case it will never be 100% accurate due to possibility of human-input errors.
I am also worried that you stack is not very well suited to do such tasks. For such processing I am utilizing Java/Groovy along with integration frameworks (Mule ESB / Spring Integration) in order to coordinate data processing.
In summary: it is really difficult and complex problem. I would rather assume less input data coverage than aiming to be 100% accurate (unless it is really worth it).