AcroLearner

Add a new Machine Learning Theme.
(language=0:English, 1:Japanese, 2:Other) 
(trainType=1:NaivebayesTextClassification, 2:LinearRegression, 3:LogisticRegression, 4:TimeSeriesAR, 5:TimeSeriesMA, 6:TimeSeriesARMA, 7:TimeSeriesARIMA)
It is also possible to add the learning theme together with its item information.
- For NaivebayesTextClassification: category is used here, and multiple items can be saved in the same category.
- For Linear Regression Prediction: at least two items are required. Put a numerical string in trainText that is separated by spaces. The splitting count of every string should be identical. The numerical string in the last trainText is the true value.
- For Logistic Regression Classification: at least two items are required. The classification values are stored in the last trainText, and they can be character strings. These numerical strings or character strings in trainText are separated by spaces, and the splitting count of every string should be identical.
- For Time Series Prediction: only a trainText is required. Put a numerical string in trainText that is separated by spaces.
If successful, please record the trainKey and resultKey.

Update the property information for a Learning Theme.
(language=0: English, 1: Japanese, 2: Other) 
(trainType=1: Naive Bayes Text Classification, 2: Linear Regression, 3: Logistic Regression, 4: Time Series AR, 5: Time Series MA, 6: Time Series ARMA, 7: Time Series ARIMA)
It is also possible to update the Learning Theme's item information. 
- For Naive Bayes Text Classification: category is required to be input here, and multiple items can be saved in the same category.
- For Linear Regression Prediction: at least two items are required. Put a numerical string in trainText that is separated with spaces. The splitting count of every string should be identical. The numerical string in the last trainText is the true value.
- For Logistic Regression Classification: at least two items are required. The classification values are stored in the last trainText, and they can be character strings or numerical strings. These strings in trainText should be separated with spaces, and the splitting count of every string should be identical.
- For Time Series Prediction: only a trainText is required. Put a numerical string in trainText that is separated with spaces.

Get the property information of Learning Theme including resultKey.

delete the Learning Theme.

Set the status of Learning Theme.(StatusId=0:new; 1:validate; 2:logical deletion)
Important: please set [StatusId] to [1] in advance before calling the following Learning/Analyse/Prediction API.

Add new items to a specified Learning Theme.
- For NaivebayesTextClassification: category is required to be input here, and multiple items can be saved in the same category.
- For Linear Regression Prediction: at least two items are required. Put a numerical string in trainText which is separated with spaces. The splitting count of every string must be identical. The numerical string in the last trainText is the true value.
- For Logistic Regression Classification: at least two items are required. The classification values are stored in the last trainText, and they can be character strings or numerical strings. These strings in trainText are separated with spaces, and the splitting count of every string must be identical.
- For Time Series Prediction: only a trainText is required. Put a numerical string in trainText which is separated with spaces.

Update certain items of a specified Learning Theme.
- For NaivebayesTextClassification: category is required to be input here, and multiple items can be saved in the same category.
- For Linear Regression Prediction: at least two items are required. Put a numerical string in trainText which is separated by spaces, and the splitting count of every string is identical. The numerical string in the last trainText is the true value.
- For Logistic Regression Classification: at least two items are required. The classification values are stored in the last trainText, and they can be character strings or numerical strings. These strings in trainText are separated by spaces, and the splitting count of every string is identical.
- For Time Series Prediction: only a trainText is required. Put a numerical string in trainText which is separated by spaces.

Get the items list of a specified Learning Theme.

Get the CSV file of items for a specified Learning Theme.

Get an Item property of a specified Learning Theme.

Delete Items of a specified Learning Theme. Multiple itemNos can be inputted.

Enter text to get the analysis results of Naive Bayes Classifier Model. The analysis may take some time depending on the number of learning items.

If you enter a numerical array, the prediction result of Linear Regression will be available.

If you enter a numerical array, the probability result of Logistic Regression Classification will be available.

Performing an Autoregressive prediction process  will make the prediction result available.
(outStartNo: starting number of the returned prediction result; outEndNo: ending number of the returned prediction result; cycleStartNo: starting number of the cycle in the learning data; cyclePeriod: cycle period in the learning data)

Performing a Moving Average prediction process will make the prediction result available.
(outStartNo: starting number of the returned prediction result; outEndNo: ending number of the returned prediction result; cycleStartNo: starting number of the cycle in the learning data; cyclePeriod: cycle period in the learning data)

Performing an Autoregressive moving average prediction process will make the prediction result available.
(outStartNo: starting number of the returned prediction result; outEndNo: ending number of the returned prediction result; cycleStartNo: starting number of the cycle in the learning data; cyclePeriod: cycle period in the learning data)

Performing an Autoregressive integrated moving average prediction process will make the prediction result available.
(outStartNo: starting number of the returned prediction result; outEndNo: ending number of the returned prediction result; cycleStartNo: starting number of the cycle in the learning data; cyclePeriod: cycle period in the learning data)

The results for mean, geometric mean, quadratic mean, median, mode, min, max, sum, count, variance, population variance, standard deviation, total deviation, total covariance, total correlation, deviation matrix, covariance matrix, Pearson's correlation matrix, Spearman's correlation matrix, and Kendall's correlation matrix will be available. In the case of Linear Regression learning, it is recommended to have a correlation value between the predictor variable and objective variable of >0.7.

The following results will be returned: mean, geometric mean, quadratic mean, median, mode, min, max, sum, count, variance, population variance, standard deviation, logarithm, logarithmic difference, ratio, logit, moving average, moving median, white noise, n-order differences, autocovariance, autocorrelation, partial autocovariance, and partial autocorrelation.
(moveInterval: Calculation period for Moving Average or Moving Median; convertMode: Conversion Mode (0: original, 1: diff, 2: log, 3: log diff, 4: ratio, 5: logit); smoothMode: Smoothing Mode (0: none, 1: moving average, 2: moving median); diffOrder: Difference floors for Calculation)