/// <summary>
/// Extract fields from a file into a numeric array for machine learning.
/// </summary>
///
/// <param name="analyst">The analyst to use.</param>
/// <param name="headers">The headers for the input data.</param>
/// <param name="csv">The CSV that holds the input data.</param>
/// <param name="outputLength">The length of the returned array.</param>
/// <param name="skipOutput">True if the output should be skipped.</param>
/// <returns>The encoded data.</returns>
public static double[] ExtractFields(EncogAnalyst analyst,
CSVHeaders headers, ReadCSV csv,
int outputLength, bool skipOutput)
{
var output = new double[outputLength];
int outputIndex = 0;
foreach (AnalystField stat in analyst.Script.Normalize.NormalizedFields)
{
if (stat.Action == NormalizationAction.Ignore)
{
continue;
}
if (stat.Output && skipOutput)
{
continue;
}
int index = headers.Find(stat.Name);
String str = csv.Get(index);
// is this an unknown value?
if (str.Equals("?") || str.Length == 0)
{
IHandleMissingValues handler = analyst.Script.Normalize.MissingValues;
double[] d = handler.HandleMissing(analyst, stat);
// should we skip the entire row
if (d == null)
{
return null;
}
// copy the returned values in place of the missing values
for (int i = 0; i < d.Length; i++)
{
output[outputIndex++] = d[i];
}
}
else
{
// known value
if (stat.Action == NormalizationAction.Normalize)
{
double d = csv.Format.Parse(str.Trim());
d = stat.Normalize(d);
output[outputIndex++] = d;
}
else
{
double[] d = stat.Encode(str.Trim());
foreach (double element in d)
{
output[outputIndex++] = element;
}
}
}
}
return output;
}
public static double[] ExtractFields(EncogAnalyst analyst, CSVHeaders headers, ReadCSV csv, int outputLength, bool skipOutput)
{
double[] numArray = new double[outputLength];
int num = 0;
using (IEnumerator<AnalystField> enumerator = analyst.Script.Normalize.NormalizedFields.GetEnumerator())
{
AnalystField field;
int num2;
string str;
IHandleMissingValues values;
double[] numArray2;
int num3;
double num4;
double num5;
double[] numArray4;
double[] numArray5;
int num6;
goto Label_0070;
Label_0022:
if (!skipOutput)
{
goto Label_02B7;
}
Label_0029:
if ((((uint) skipOutput) + ((uint) num6)) > uint.MaxValue)
{
goto Label_00B1;
}
goto Label_0070;
Label_0043:
if ((((uint) num4) - ((uint) outputLength)) > uint.MaxValue)
{
goto Label_0022;
}
if (((uint) num3) > uint.MaxValue)
{
goto Label_022A;
}
Label_0070:
if (enumerator.MoveNext())
{
goto Label_02CD;
}
goto Label_01C2;
Label_007E:
if (((uint) num2) > uint.MaxValue)
{
goto Label_021B;
}
goto Label_0043;
Label_009A:
if (!field.Output)
{
goto Label_02B7;
}
goto Label_0022;
Label_00B1:
num5 = numArray5[num6];
Label_00B9:
numArray[num++] = num5;
num6++;
Label_00C8:
if (num6 < numArray5.Length)
{
goto Label_00B1;
}
if ((((uint) num3) + ((uint) num5)) >= 0)
{
goto Label_0116;
}
Label_00E8:
numArray5 = field.Encode(str.Trim());
num6 = 0;
if ((((uint) num6) + ((uint) num5)) >= 0)
{
goto Label_00C8;
}
Label_0116:
if ((((uint) num4) - ((uint) num5)) <= uint.MaxValue)
{
goto Label_007E;
}
goto Label_01C2;
Label_0138:
if (field.Action == NormalizationAction.Normalize)
{
num4 = csv.Format.Parse(str.Trim());
num4 = field.Normalize(num4);
numArray[num++] = num4;
if ((((uint) outputLength) - ((uint) num6)) > uint.MaxValue)
{
goto Label_022A;
}
goto Label_0070;
}
if ((((uint) num2) - ((uint) outputLength)) <= uint.MaxValue)
{
goto Label_00E8;
}
goto Label_01C2;
Label_01A4:
if (num3 < numArray2.Length)
{
goto Label_0207;
}
goto Label_0070;
Label_01B1:
if (str.Length == 0)
{
goto Label_025F;
}
goto Label_0138;
Label_01C2:
if ((((uint) num6) - ((uint) num3)) >= 0)
{
return numArray;
}
if ((((uint) num2) - ((uint) num5)) <= uint.MaxValue)
{
goto Label_02CD;
}
goto Label_0295;
Label_0202:
num3 = 0;
goto Label_01A4;
Label_0207:
numArray[num++] = numArray2[num3];
num3++;
goto Label_02E6;
Label_021B:
numArray2 = values.HandleMissing(analyst, field);
if (numArray2 != null)
{
goto Label_0202;
}
Label_022A:
numArray4 = null;
if ((((uint) outputLength) | 8) != 0)
{
return numArray4;
}
goto Label_02E6;
Label_024E:
if (!str.Equals("?"))
{
goto Label_01B1;
}
Label_025F:
values = analyst.Script.Normalize.MissingValues;
goto Label_021B;
Label_0273:
if (((uint) num5) > uint.MaxValue)
{
goto Label_00B9;
}
str = csv.Get(num2);
goto Label_024E;
Label_0295:
if ((((uint) num5) | uint.MaxValue) == 0)
{
goto Label_0029;
}
goto Label_0022;
Label_02B7:
num2 = headers.Find(field.Name);
goto Label_0273;
Label_02CD:
field = enumerator.Current;
if (field.Action == NormalizationAction.Ignore)
{
goto Label_0070;
}
goto Label_009A;
Label_02E6:
if ((((uint) num4) - ((uint) skipOutput)) >= 0)
{
goto Label_01A4;
}
}
return numArray;
}
