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, 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 the trainText field, separated by spaces. The splitting count of every string should be identical. The numerical string in the last trainText field is the true value.
- For Logistic Regression Classification: at least two items are required. The classification values are stored in the last trainText field, and they can be character strings. These numerical or character strings in the trainText field should be separated by spaces, with identical splitting counts.
- For Time Series Prediction: only trainText is required. Put a numerical string in the trainText field, separated by spaces.
If successful, please record the trainKey and resultKey.

Update the property information of a 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 update the learning theme together with its item information. 
- 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 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 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 which is separated with spaces.

Retrieve the property information of a Learning Theme including the 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 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.

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 that is separated by spaces. 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. These numerical strings or character 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 that is separated by spaces.

Get the items list of a specified Learning Theme.

Retrieve the CSV file containing the items of a specified Learning Theme.

Get an Item property of a specified Learning Theme.

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

Enter a text to get the analysis result of the Naive Bayes Classifier Model. The processing time may vary depending on the number of learning items.

Input a numerical array to get the prediction result of Linear Regression.

Enter a numerical array and the probability result of Logistic Regression Classification will be available.

Autoregressive prediction processing is performed, and the prediction result will be available.
(outStartNo: Start number of the returned prediction result; outEndNo: End number of the returned prediction result; cycleStartNo: Cycle start number in learning data; cyclePeriod: Cycle period in learning data)

Moving Average Prediction processing has been performed, and the prediction result will be available.
(outStartNo: Starting number of the returned prediction result; outEndNo: Ending number of the returned prediction result; cycleStartNo: Cycle Starting number in Learning data; cyclePeriod: Cycle period in Learning data)

Autoregressive moving average prediction processing has been performed, and the prediction result will be available.
(outStartNo: Start number of the returning prediction result; outEndNo: End number of the returning prediction result; cycleStartNo: Cycle Start number in Learning data; cyclePeriod: Cycle period in Learning data)

Autoregressive integrated moving average prediction processing has been performed, and the prediction result is now available. 
(outStartNo: Start number of the returned prediction result; outEndNo: End number of the returned prediction result; cycleStartNo: Cycle start number in learning data; cyclePeriod: Cycle period in 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, a correlation value greater than 0.7 between the predictor variable and the objective variable is recommended.

The following results will be returned: mean, geometric mean, quadratic mean, median, mode, minimum value, maximum value, 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: logdiff, 4: ratio, 5: logit); smoothMode: Smoothing mode (0: none, 1: moving average, 2: moving median); diffOrder: Difference floors for calculation)