public void Z_Score_Test()
{
StatisticCalculator StatCalc = new StatisticCalculator();
var finalZSCore = StatCalc.ZScore(a, arrayA);
Assert.AreEqual(0.77, finalZSCore);
}
public void Variance_Test()
{
StatisticCalculator StatCalc = new StatisticCalculator();
var finalVariance = StatCalc.Variance(arrayA);
Assert.AreEqual(6.8, finalVariance);
}
public void StandardDev_Test()
{
StatisticCalculator StatCalc = new StatisticCalculator();
var finalStandardDev = StatCalc.StandardDeviation(arrayA);
Assert.AreEqual(2.61, finalStandardDev);
}
public void Mean_AverageTest()
{
StatisticCalculator StatCalc = new StatisticCalculator();
var finalMean = StatCalc.Mean_Average(arrayX);
Assert.AreEqual(6.57, finalMean);
}
/// <summary>
/// Returns true or false depending on if a string is detected in a array list of arrayLists. used by GetSelectedData().
/// </summary>
/// <param name="arrayListOfArrayLists"></param>
/// <returns></returns>
public bool StringDetection(ArrayList[] arrayListOfArrayLists)
{
try
{
//If a string is detected in a selectedColumn. Set bool stringDetected to true
for (int columnIndex = 0; columnIndex < arrayListOfArrayLists.Length; columnIndex++)
{
try
{
if (StatisticCalculator.ConvertToList(arrayListOfArrayLists[columnIndex])[0] == -1)//-1 == string
{
stringArrayIndex = columnIndex;
return(true);
}
}
catch (ArgumentOutOfRangeException)
{
return(false);
}
}
}
catch (NullReferenceException nRe)
{
throw nRe;
}
return(false);
}
/// <summary>
/// Add points to the plotter
/// </summary>
/// <param name="points"></param>
public void AddPoints(ArrayList[] points)
{
this.points = points;
this.Series[serieOne].Points.Clear();
this.Series[serieTwo].Points.Clear();
//Should be two arraylist which are both the same size and not empty.
if (points.Length < 3 && points.Length > 0 && points[0].Count > 0 && points[1].Count > 0 && points[0].Count == points[1].Count)
{
//if noth columns are numbers.
try
{
for (int i = 0; i < points[0].Count; i++)
{
this.Series[serieOne].Points.AddXY(Convert.ToInt32(points[0][i]), Convert.ToInt32(points[1][i]));
}
}
//if column is a string.
catch (Exception)
{
this.Series[serieOne].Points.Clear();
for (int i = 0; i < points[0].Count; i++)
{
this.Series[serieOne].Points.AddXY(points[0][i], points[1][i]);
}
}
}
//set gradientline.
gradient = Math.Round(StatisticCalculator.getGradient(points), 3);
int max = getMax(points[0]);
int min = getMin(points[0]);
this.Series[serieTwo].Points.AddXY(min, (min * gradient));
this.Series[serieTwo].Points.AddXY(max, (max * gradient));
}
public void MeanTest()
{
StatisticCalculator statsCal = new StatisticCalculator();
int[] values = { 1, 2 };
var mean = statsCal.Mean(values);
Assert.AreEqual(1.5, mean);
}
static void Main(string[] args)
{
var statisticCalculator = new StatisticCalculator(TextFileName);
IStatisticPrinter printer = new ConsolePrinter();
Statistic statistic = statisticCalculator.CalculateUsingSingleThread();
printer.Print(statistic);
statistic = statisticCalculator.CalculateUsingAllThreads();
printer.Print(statistic);
}
/// <summary>
/// Create a staticModel from a given array list. used by GetSelectedData().
/// </summary>
/// <param name="arrayList"></param>
public StatisticModel CreateStatisticModelFromArrayList(ArrayList arrayList)
{
//Calculate data
double average = Math.Round(StatisticCalculator.Average(arrayList), 2);
double standardDeviation = Math.Round(StatisticCalculator.StandardDeviation(arrayList), 2);
int lowerWhisker = StatisticCalculator.SmallestValue(arrayList);
double lowerBox = StatisticCalculator.FirstQuartile(arrayList);
double median = StatisticCalculator.Median(arrayList);
double upperBox = StatisticCalculator.ThirdQuartile(arrayList);
int upperWhisker = StatisticCalculator.BiggestValue(arrayList);
//Create statisticsModel and return it.
return(new StatisticModel(average, standardDeviation, lowerWhisker, lowerBox, median, upperBox, upperWhisker));
}
/// <summary>
/// Returns a Array with arraylists ith the content of the selected columns
/// </summary>
/// <param name="dataGrid">The dataGrid with the selected data.</param>
/// <returns></returns>
public ArrayList[] getResultSelection(CustomDataGridView dataGrid)
{
ArrayList[] lijsten = new ArrayList[2];
lijsten[0] = new ArrayList();
lijsten[1] = new ArrayList();
//Get number of unique columns.
try
{
List <int> columns = new List <int>();
foreach (DataGridViewTextBoxCell item in dataView.SelectedCells)
{
columns.Add(item.ColumnIndex);
}
var unique_items = new HashSet <int>(columns);
List <int> uniekeColumns = new List <int>();
foreach (int i in unique_items)
{
uniekeColumns.Add(i);
}
//if 1 of 2 colomns are selected.
if (uniekeColumns.Count < 3 && uniekeColumns.Count > 0)
{
int column1 = uniekeColumns[0];
int column2 = uniekeColumns.Count == 2 ? uniekeColumns[1] : 0;
ArrayList listColomn1 = new ArrayList();
ArrayList listColomn2 = new ArrayList();
//add to column resultList.
foreach (DataGridViewTextBoxCell item in dataView.SelectedCells)
{
if (item.ColumnIndex == column1)
{
listColomn1.Add(item.Value.ToString());
}
if (item.ColumnIndex == column2)
{
listColomn2.Add(item.Value.ToString());
}
}
//if both resultList are the same size or list2 is empty, set the lists to the arrayListOfArrayLists.
if (listColomn1.Count == listColomn2.Count || listColomn2.Count == 0)
{
listColomn1.Reverse();
listColomn2.Reverse();
lijsten[0] = (listColomn1);
lijsten[1] = (listColomn2);
foreach (ArrayList a in lijsten)
{
foreach (String s in a)
{
Console.WriteLine(s);
}
Console.WriteLine();
}
List <int> lijst1 = StatisticCalculator.ConvertToList(lijsten[0]);
List <int> lijst2 = new List <int>();
if (listColomn2.Count != 0)
{
lijst2 = StatisticCalculator.ConvertToList(lijsten[1]);
}
//If the selection is only a string or string + string give a message
if (lijst1[0] == -1 && lijst2.Count == 0 || (lijst1[0] == -1 && lijst2[0] == -1))
{
MessageBox.Show("Selecteer a.u.b. ook getallen.", "Selectie bevat geen getallen.", MessageBoxButtons.OK, MessageBoxIcon.Warning);
mainController.statisticView.ClearData();
//mainController.mainViewController.SelectTab(1);
lijsten = null;
return(lijsten);
}
}
else
{
MessageBox.Show("Selecteer twee evenlange kolommen a.u.b.", "Kolommen zijn niet even lang.", MessageBoxButtons.OK, MessageBoxIcon.Warning);
mainController.statisticView.ClearData();
}
}
else
{
MessageBox.Show("Selecteer a.u.b. eerst één of twee kolommen in de resultaten tap.", "Er kan geen scatter plot van uw selectie gemaakt worden.", MessageBoxButtons.OK, MessageBoxIcon.Warning);
mainController.statisticView.ClearData();
}
}
catch (Exception)
{
MessageBox.Show("Sellecteer alleen kolom met tekst a.u.b.", "U mag geen knopjes selecteren.", MessageBoxButtons.OK, MessageBoxIcon.Warning);
mainController.statisticView.ClearData();
}
return(lijsten);
}
public async Task <IActionResult> AnalyzeDataAsync(
[HttpTrigger(AuthorizationLevel.Anonymous, "get", "post", Route = "analytics/plotly/{networkId}/{interval}")] HttpRequest req,
string networkId,
int interval,
ILogger log)
{
string body = await new StreamReader(req.Body).ReadToEndAsync();
Dictionary <string, int> continueAtIteration;
try
{
continueAtIteration = JsonConvert.DeserializeObject <Dictionary <string, int> >(body);
}
catch (Exception)
{
return(new BadRequestResult());
}
if (continueAtIteration == null)
{
continueAtIteration = new Dictionary <string, int>();
}
var query = _logContainer
.GetItemLinqQueryable <LogEntryStack>(allowSynchronousQueryExecution: true, requestOptions: new QueryRequestOptions {
PartitionKey = new PartitionKey(networkId)
})
.Where(o => o.Timestamp >= DateTime.UtcNow.AddSeconds(-interval));
var iterator = query.ToFeedIterator();
var documents = new List <LogEntryStack>();
while (iterator.HasMoreResults)
{
documents.AddRange((await iterator.ReadNextAsync()).ToList());
}
if (documents.Count <= 0)
{
return(new OkObjectResult(new { data = new List <PlotyTrace>(), lastIteration = continueAtIteration, statistics = new List <PlotyTrace>() }));
}
var logEntries = documents
.SelectMany(o => o.LogEntries)
.Where(o => o.Iteration > (continueAtIteration.ContainsKey(o.NodeId) ? continueAtIteration[o.NodeId] : -1))
.ToList();
var traces = logEntries
.OrderBy(o => o.Iteration)
.GroupBy(o => o.NodeId)
.Select(o => new PlotyTrace {
name = o.Key, x = o.ToList().Select(k => (double)k.Iteration).ToList(), y = o.ToList().Select(s => s.State).ToList(), line = new PlotTraceLine {
width = 1
}, type = "scatter"
})
.ToList();
var maxIterations = continueAtIteration;
traces
.ForEach(t => maxIterations[t.name] = (int)t.x.Max());
var statistics = documents
.SelectMany(o => o.LogEntries)
.Where(o => o.Iteration <= maxIterations.Where(t => t.Key != "average").Min(t => t.Value))
.Where(o => o.Iteration > (continueAtIteration.ContainsKey("average") ? continueAtIteration["average"] : -1))
.GroupBy(o => o.Iteration)
.OrderBy(o => o.Key)
.Select(o =>
{
var avgRef = o.ToList().Select(s => s.ReferenceSignal).ToList().Average();
var avgState = o.ToList().Select(s => s.State).ToList().Average();
var maxState = o.ToList().Max(d => d.State);
var minState = o.ToList().Min(d => d.State);
var convergenceIterations = documents
.SelectMany(c => c.LogEntries)
.Where(c => c.Iteration >= o.Key - 2 && c.Iteration <= o.Key + 1) // range for required states
.GroupBy(c => c.NodeId)
.Where(c => c.ToList().Count == 4) // enough items in to calculate
.Where(c => Math.Abs(c.ToList().Max(z => z.ReferenceSignal) - c.ToList().Min(z => z.ReferenceSignal)) < 0.001) // noch change in ref
.Select(c => new
{
NodeID = c.Key,
ConvergenceRate = StatisticCalculator.CalculateConvergenceRate(
c.ToList()
.OrderBy(x => x.Iteration)
.Select(x => x.State)
.ToArray())
})
.ToList();
return(new
{
Iteration = o.Key,
Average = avgRef,
Error = StatisticCalculator.CalculatePercentError(avgState, avgRef),
Difference = Math.Abs(maxState) + Math.Abs(minState) > 0 ? StatisticCalculator.CalculatePercentDifferencePeak(o.ToList().Select(r => r.State).ToArray()) : 0,
ConvergenceRate = convergenceIterations.Count > 0 ? convergenceIterations.Average(c => c.ConvergenceRate) : double.NaN
});
})
.ToList();
if (statistics.Count > 0)
{
traces.Insert(0, new PlotyTrace
{
name = "average",
x = statistics.Select(o => (double)o.Iteration).ToList(),
y = statistics.Select(o => o.Average).ToList()
});
}
var lastIterations = continueAtIteration;
traces.ForEach(o => lastIterations[o.name] = (int)o.x.Max());
var statisticTraces = new List <PlotyTrace>();
statisticTraces.Add(new PlotyTrace
{
name = "%Error",
x = statistics.Where(s => s.Error.HasValue).Select(s => (double)s.Iteration).ToList(),
y = statistics.Where(s => s.Error.HasValue).Select(s => s.Error.Value).ToList()
});
statisticTraces.Add(new PlotyTrace
{
name = "%Difference",
x = statistics.Select(s => (double)s.Iteration).ToList(),
y = statistics.Select(s => s.Difference).ToList()
});
statisticTraces.Add(new PlotyTrace
{
name = "Convergence",
x = statistics.Where(s => !double.IsNaN(s.ConvergenceRate)).Select(s => (double)s.Iteration).ToList(),
y = statistics.Where(s => !double.IsNaN(s.ConvergenceRate)).Select(s => s.ConvergenceRate).ToList()
});
var result = new
{
data = traces,
statistics = statisticTraces,
lastIterations = lastIterations
};
return(new OkObjectResult(result));
}
