Numeric Processing ​
The following API endpoints perform analysis on numeric input.
Trend Analysis ​
Analyse and predict trends using regression by inputting a set of numbers, some numeric lables, and a prediction value.
Parameters
endpoint : https://api.weburban.com/numeric/to-trenddata : JSON array of numberslabels : Numeric array of numbers as labelspredict : JSON array of numbers used to predict a labelregression : The type of regression which can be linear or logistic
Example of how this would be implemented in shown below.
Explain what these numbers in mean
In the example below, the data provided are two arbitrary numbers [a, b] put together in a set of other arbitrary numbers as the inputs for data. Each set of numbers [a, b] corresponds with the label in order. In this example, a multiplied by b is the label value. The predict value of [[2,7]] asks the linear regression model to predict the label without knowing how the numbers are related.
curl --location 'https://api.weburban.com/text/to-vector' \
--header 'Accept: application/json' \
--header 'x-api-key: <API Key>' \
--header 'Content-Type: application/json' \
--data '{
"data" : [[2,1], [2,2], [2,3], [2,4], [2,5], [2,6]],
"labels" : [2,4,6,8,10,12],
"predict" : [[2, 7]],
"regression" : "linear"
}'curl --location 'https://api.weburban.com/text/to-vector' \
--header 'Accept: application/json' \
--header 'x-api-key: <API Key>' \
--header 'Content-Type: application/json' \
--data '{
"data" : [[2,1], [2,2], [2,3], [2,4], [2,5], [2,6]],
"labels" : [2,4,6,8,10,12],
"predict" : [[2, 7]],
"regression" : "linear"
}'var myHeaders = new Headers();
myHeaders.append("Accept", "application/json");
myHeaders.append("x-api-key", "<API Key>");
myHeaders.append("Content-Type", "application/json");
var raw = JSON.stringify({
"data" : [[2,1], [2,2], [2,3], [2,4], [2,5], [2,6]],
"labels" : [2,4,6,8,10,12],
"predict" : [[2, 7]],
"regression" : "linear"
});
var requestOptions = {
method: 'POST',
headers: myHeaders,
body: raw,
redirect: 'follow'
};
fetch("https://api.weburban.com/text/to-vector", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));var myHeaders = new Headers();
myHeaders.append("Accept", "application/json");
myHeaders.append("x-api-key", "<API Key>");
myHeaders.append("Content-Type", "application/json");
var raw = JSON.stringify({
"data" : [[2,1], [2,2], [2,3], [2,4], [2,5], [2,6]],
"labels" : [2,4,6,8,10,12],
"predict" : [[2, 7]],
"regression" : "linear"
});
var requestOptions = {
method: 'POST',
headers: myHeaders,
body: raw,
redirect: 'follow'
};
fetch("https://api.weburban.com/text/to-vector", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));The output of the request above will produce the following response. Note that the prediction is 14.000000000000004 and not 14. This is because it is predicting the outcome of [2, 7] and not doing a multiplication.
{
"Version": "1.0",
"Output": {
"regression": "linear",
"prediction": 14.000000000000004
}
}{
"Version": "1.0",
"Output": {
"regression": "linear",
"prediction": 14.000000000000004
}
}