public LinRegressionResults GetRegressionResult(GadgetParameters gadgetOptions, List <string> columnNames, List <DictionaryDTO> inputDtoList, IEnumerable <EwavDataFilterCondition> ewavDataFilters, List <EwavRule_Base> rules, string filterString = "")
{
if (gadgetOptions.UseAdvancedDataFilter)
{
dh = new DashboardHelper(gadgetOptions, filterString, rules);
gadgetOptions.UseAdvancedDataFilter = true;
gadgetOptions.AdvancedDataFilterText = filterString;
}
else
{
dh = new DashboardHelper(gadgetOptions, ewavDataFilters, rules);
}
DataTable dt;
LinRegressionResults results = new LinRegressionResults();
try
{
dt = dh.GenerateTable(columnNames, gadgetOptions);
}
catch (Exception e)
{
results.ErrorMessage = e.Message;
return(results);
}
Dictionary <string, string> inputVariableList = RegressionManager.ConvertDtoToDic(inputDtoList);
StatisticsRepository.LinearRegression linearRegression = new StatisticsRepository.LinearRegression();
StatisticsRepository.LinearRegression.LinearRegressionResults regressionResults = linearRegression.LinearRegression(inputVariableList, dt);
results.RegressionResults = RegressionManager.ConvertToLinRegResults(regressionResults);
results.CorrelationCoefficient = results.RegressionResults.CorrelationCoefficient;
results.RegressionDf = results.RegressionResults.RegressionDf;
results.RegressionF = results.RegressionResults.RegressionF;
results.RegressionMeanSquare = results.RegressionResults.RegressionMeanSquare;
results.RegressionSumOfSquares = results.RegressionResults.RegressionSumOfSquares;
results.ResidualsDf = results.RegressionResults.ResidualsDf;
results.ResidualsMeanSquare = results.RegressionResults.ResidualsMeanSquare;
results.ResidualsSumOfSquares = results.RegressionResults.ResidualsSumOfSquares;
results.TotalDf = results.RegressionResults.TotalDf;
results.TotalSumOfSquares = results.RegressionResults.TotalSumOfSquares;
results.Variables = results.RegressionResults.Variables;
if (results.RegressionResults.ErrorMessage != null)
{
results.ErrorMessage = results.RegressionResults.ErrorMessage.Replace("<tlt>", string.Empty).Replace("</tlt>", string.Empty);
}
return(results);
}
/// <summary>
/// GetFactory that converts LogisticRegressionResults values to LogRessionResults(custom) class.
/// </summary>
/// <param name="logisticRegressionResults"></param>
/// <returns></returns>
internal static LinRegressionResults ConvertToLinRegResults(StatisticsRepository.LinearRegression.LinearRegressionResults linearRegressionResults)
{
LinRegressionResults linRegResults = new LinRegressionResults();
linRegResults.CorrelationCoefficient = linearRegressionResults.correlationCoefficient;
linRegResults.RegressionDf = linearRegressionResults.regressionDf;
linRegResults.RegressionF = linearRegressionResults.regressionF;
linRegResults.RegressionMeanSquare = linearRegressionResults.regressionMeanSquare;
linRegResults.RegressionSumOfSquares = linearRegressionResults.regressionSumOfSquares;
linRegResults.ResidualsDf = linearRegressionResults.residualsDf;
linRegResults.ResidualsMeanSquare = linearRegressionResults.residualsMeanSquare;
linRegResults.ResidualsSumOfSquares = linearRegressionResults.residualsSumOfSquares;
linRegResults.TotalDf = linearRegressionResults.totalDf;
linRegResults.TotalSumOfSquares = linearRegressionResults.totalSumOfSquares;
linRegResults.Variables = ConvertToVariableClass(linearRegressionResults.variables);
linRegResults.ErrorMessage = linearRegressionResults.errorMessage;
return(linRegResults);
}
public string BuildInputParams(
string chartType,
string chartTitle,
string independentTitle,
string dependentTitle,
string independentValueFormat,
string dependentValueFormat,
string interval,
string intervalUnit,
object startFrom,
DataTable regressionTable)
{
StringBuilder chartLineSeries = new StringBuilder();
string independentValueType = "";
string dependentValueType = "";
chartType = chartType.Replace("\"", "");
chartTitle = chartTitle.Replace("\"", "");
independentTitle = independentTitle.Replace("\"", "");
dependentTitle = dependentTitle.Replace("\"", "");
switch (chartType.ToUpperInvariant().Replace(" ", ""))
{
case SilverlightStatics.Area:
chartType = SilverlightStatics.Area;
break;
case SilverlightStatics.Column:
chartType = SilverlightStatics.Bar;
break;
case SilverlightStatics.Bubble:
chartType = SilverlightStatics.Bubble;
break;
case SilverlightStatics.EpiCurve:
chartType = SilverlightStatics.Histogram;
break;
case SilverlightStatics.Bar:
chartType = SilverlightStatics.RotatedBar;
break;
case SilverlightStatics.Line:
chartType = SilverlightStatics.Line;
break;
case SilverlightStatics.Pie:
chartType = SilverlightStatics.Pie;
break;
case SilverlightStatics.Scatter:
chartType = SilverlightStatics.Scatter;
break;
case SilverlightStatics.Stacked:
chartType = SilverlightStatics.Stacked;
break;
case SilverlightStatics.TreeMap:
chartType = SilverlightStatics.TreeMap;
break;
default:
chartType = SilverlightStatics.Bar;
break;
}
chartLineSeries.Append("chartLineSeries=");
DataTable distinct = regressionTable.DefaultView.ToTable(true, "SeriesName");
if (regressionTable.Rows.Count == 0)
{
return(string.Empty);
}
foreach (DataRow row in distinct.Rows)
{
string seriesName = row["SeriesName"].ToString();
string filter = string.Format("SeriesName = '{0}'", seriesName);
DataRow[] regressionData = regressionTable.Select(filter);
if (chartLineSeries.ToString().EndsWith("chartLineSeries=") == false)
{
chartLineSeries.Append(SilverlightStatics.SeparateLineSeries);
}
chartLineSeries.Append("(");
chartLineSeries.Append("LineSeriesTitle");
chartLineSeries.Append(seriesName);
chartLineSeries.Append("LineSeriesTitle");
Type indepValueType = typeof(string);
indepValueType = regressionData[0]["Predictor"].GetType();
independentValueType = indepValueType.ToString();
chartLineSeries.Append(SilverlightStatics.IndependentValueFormat);
chartLineSeries.Append(independentValueFormat);
chartLineSeries.Append(SilverlightStatics.IndependentValueFormat);
chartLineSeries.Append(SilverlightStatics.DependentValueFormat);
chartLineSeries.Append(dependentValueFormat);
chartLineSeries.Append(SilverlightStatics.DependentValueFormat);
chartLineSeries.Append(SilverlightStatics.LineSeriesDataString);
foreach (DataRow regression in regressionData)
{
if (regression["Predictor"] is DateTime)
{
string dateTimeString = ((DateTime)regression["Predictor"]).ToString(_dateTimeFormat);
chartLineSeries.Append(dateTimeString);
}
else
{
chartLineSeries.Append(regression["Predictor"].ToString());
}
chartLineSeries.Append(SilverlightStatics.SeparateIndDepValues);
chartLineSeries.Append(regression["Response"].ToString());
chartLineSeries.Append(SilverlightStatics.SeparateDataPoints);
}
if (chartLineSeries.ToString().EndsWith(SilverlightStatics.SeparateDataPoints))
{
string series = chartLineSeries.ToString().Substring(0, chartLineSeries.Length - SilverlightStatics.SeparateDataPoints.Length);
chartLineSeries = new StringBuilder();
chartLineSeries.Append(series);
}
chartLineSeries.Append(SilverlightStatics.LineSeriesDataString);
}
if (chartType == SilverlightStatics.Scatter)
{
List <NumericDataValue> dataValues = new List <NumericDataValue>();
NumericDataValue minValue = null;
NumericDataValue maxValue = null;
foreach (DataRow row in regressionTable.Rows)
{
if (row["Response"].Equals(DBNull.Value) || row["Predictor"].Equals(DBNull.Value))
{
continue;
}
NumericDataValue currentValue = new NumericDataValue()
{
DependentValue = Convert.ToDecimal(row["Response"]), IndependentValue = Convert.ToDecimal(row["Predictor"])
};
dataValues.Add(currentValue);
if (minValue == null)
{
minValue = currentValue;
}
else
{
if (currentValue.IndependentValue < minValue.IndependentValue)
{
minValue = currentValue;
}
}
if (maxValue == null)
{
maxValue = currentValue;
}
else
{
if (currentValue.IndependentValue > maxValue.IndependentValue)
{
maxValue = currentValue;
}
}
}
StatisticsRepository.LinearRegression linearRegression = new StatisticsRepository.LinearRegression();
Dictionary <string, string> inputVariableList = new Dictionary <string, string>();
inputVariableList.Add("Response", "dependvar");
inputVariableList.Add("intercept", "true");
inputVariableList.Add("includemissing", "false");
inputVariableList.Add("p", "0.95");
inputVariableList.Add("Predictor", "unsorted");
StatisticsRepository.LinearRegression.LinearRegressionResults regresResults = linearRegression.LinearRegression(inputVariableList, regressionTable);
object[] result = new object[] { dataValues, regresResults, maxValue, minValue };
decimal coefficient = Convert.ToDecimal(regresResults.variables[0].coefficient);
decimal constant = Convert.ToDecimal(regresResults.variables[1].coefficient);
NumericDataValue newMaxValue = new NumericDataValue();
newMaxValue.IndependentValue = maxValue.IndependentValue + 1;
newMaxValue.DependentValue = (coefficient * maxValue.IndependentValue) + constant;
NumericDataValue newMinValue = new NumericDataValue();
newMinValue.IndependentValue = minValue.IndependentValue - 1;
newMinValue.DependentValue = (coefficient * minValue.IndependentValue) + constant;
chartLineSeries.Append("LineSeriesDataString)^((LineSeriesTitleLinear RegressionLineSeriesTitleIndependentValueFormatIndependentValueFormatDependentValueFormatDependentValueFormatLineSeriesDataString");
chartLineSeries.Append(newMinValue.IndependentValue.ToString());
chartLineSeries.Append("^sidv^");
chartLineSeries.Append(newMinValue.DependentValue.ToString());
chartLineSeries.Append("^&^");
chartLineSeries.Append(newMaxValue.IndependentValue.ToString());
chartLineSeries.Append("^sidv^");
chartLineSeries.Append(newMaxValue.DependentValue.ToString());
chartLineSeries.Append("LineSeriesDataString");
}
chartLineSeries.Append(")");
string inputParams = string.Empty;
if (chartType.Length > 0)
{
inputParams += string.Format("chartType={0},", chartType);
}
if (chartTitle.Length > 0)
{
inputParams += string.Format("chartTitle={0},", chartTitle);
}
if (independentTitle.Length > 0)
{
inputParams += string.Format("independentLabel={0},", independentTitle);
}
if (dependentTitle.Length > 0)
{
inputParams += string.Format("dependentLabel={0},", dependentTitle);
}
if (independentValueFormat.Length > 0)
{
inputParams += string.Format("independentValueFormat={0},", independentValueFormat);
}
if (dependentValueFormat.Length > 0)
{
inputParams += string.Format("dependentValueFormat={0},", dependentValueFormat);
}
if (independentValueType.Length > 0)
{
inputParams += string.Format("independentValueType={0},", independentValueType);
}
if (dependentValueType.Length > 0)
{
inputParams += string.Format("dependentValueType={0},", dependentValueType);
}
if (interval.Length > 0)
{
inputParams += string.Format("interval={0},", interval);
}
if (intervalUnit.Length > 0)
{
inputParams += string.Format("intervalUnit={0},", intervalUnit);
}
if (startFrom is DateTime)
{
startFrom = ((DateTime)startFrom).ToString(_dateTimeFormat);
}
else
{
startFrom = startFrom.ToString();
}
if (((string)startFrom).Length > 0)
{
inputParams += string.Format("startFrom={0},", ((string)startFrom));
}
inputParams += chartLineSeries.Replace(",", SilverlightStatics.Comma);
return(inputParams);
}
public ScatterDataDTO GenerateTable(GadgetParameters gadgetOptions,
IEnumerable <EwavDataFilterCondition> ewavDataFilters, List <EwavRule_Base> rules, string filterString = "")
{
List <ListOfStringClass> lls = new List <ListOfStringClass>();
if (gadgetOptions.UseAdvancedDataFilter)
{
dh = new DashboardHelper(gadgetOptions, filterString, rules);
gadgetOptions.UseAdvancedDataFilter = true;
gadgetOptions.AdvancedDataFilterText = filterString;
}
else
{
dh = new DashboardHelper(gadgetOptions, ewavDataFilters, rules);
}
MyString ms = new MyString();
string xAxisVar = gadgetOptions.MainVariableName;
string yAxisVar = gadgetOptions.CrosstabVariableName;
List <string> columns = new List <string>();
columns.Add(xAxisVar);
columns.Add(yAxisVar);
DataTable dt = dh.GenerateTable(columns, gadgetOptions);
List <NumericDataValue> dataValues = new List <NumericDataValue>();
NumericDataValue minValue = null;
NumericDataValue maxValue = null;
//regressTable.FieldsList.Fields
//foreach (DataRow row in regressTable.Rows)
foreach (DataRow row in dt.Rows)
{
//if (regressTable.GetValueAtRow(yAxisVar, row).Equals(DBNull.Value) || regressTable.GetValueAtRow(xAxisVar, row).Equals(DBNull.Value))
if (row[yAxisVar].Equals(DBNull.Value) || row[xAxisVar].Equals(DBNull.Value))
{
continue;
}
NumericDataValue currentValue = new NumericDataValue()
{
DependentValue = Convert.ToDecimal(row[yAxisVar]), IndependentValue = Convert.ToDecimal(row[xAxisVar])
};
//NumericDataValue currentValue = new NumericDataValue() { DependentValue = Convert.ToDecimal(regressTable.GetValueAtRow(yAxisVar, row)), IndependentValue = Convert.ToDecimal(regressTable.GetValueAtRow(xAxisVar, row)) };
dataValues.Add(currentValue);
if (minValue == null)
{
minValue = currentValue;
}
else
{
if (currentValue.IndependentValue < minValue.IndependentValue)
{
minValue = currentValue;
}
}
if (maxValue == null)
{
maxValue = currentValue;
}
else
{
if (currentValue.IndependentValue > maxValue.IndependentValue)
{
maxValue = currentValue;
}
}
}
StatisticsRepository.LinearRegression linearRegression = new StatisticsRepository.LinearRegression();
Dictionary <string, string> inputVariableList = new Dictionary <string, string>();
inputVariableList.Add(yAxisVar, "dependvar");
inputVariableList.Add("intercept", "true");
inputVariableList.Add("includemissing", "false");
inputVariableList.Add("p", "0.95");
inputVariableList.Add(xAxisVar, "unsorted");
StatisticsRepository.LinearRegression.LinearRegressionResults regresResults = linearRegression.LinearRegression(inputVariableList, dt);
LinRegressionResults results = new LinRegressionResults();
results = RegressionManager.ConvertToLinRegResults(regresResults);
//results.CorrelationCoefficient = results.RegressionResults.CorrelationCoefficient;
//results.RegressionDf = results.RegressionResults.RegressionDf;
//results.RegressionF = results.RegressionResults.RegressionF;
//results.RegressionMeanSquare = results.RegressionResults.RegressionMeanSquare;
//results.RegressionSumOfSquares = results.RegressionResults.RegressionSumOfSquares;
//results.ResidualsDf = results.RegressionResults.ResidualsDf;
//results.ResidualsMeanSquare = results.RegressionResults.ResidualsMeanSquare;
//results.ResidualsSumOfSquares = results.RegressionResults.ResidualsSumOfSquares;
//results.TotalDf = results.RegressionResults.TotalDf;
//results.TotalSumOfSquares = results.RegressionResults.TotalSumOfSquares;
//results.Variables = results.RegressionResults.Variables;
if (results.ErrorMessage != null)
{
results.ErrorMessage = results.ErrorMessage.Replace("<tlt>", string.Empty).Replace("</tlt>", string.Empty);
}
ScatterDataDTO scatterDTO = new ScatterDataDTO();
scatterDTO.DataValues = dataValues;
scatterDTO.RegresResults = results;
scatterDTO.MinValue = minValue;
scatterDTO.MaxValue = maxValue;
//RegressionManager lrm = new RegressionManager();
//lls = lrm.ConvertDataTableToList(dt);
//return new List<ListOfStringClass>();
return(scatterDTO);
}
