private void btnOK_Click(object sender, EventArgs e)
{
frmWaitDialog frmWait = new frmWaitDialog("正在汇总...", "提示信息");
try
{
frmWait.Owner = this;
frmWait.TopMost = false;
SampleSummary summary = new SampleSummary(_pSampleLayer, _pSurveyLayer, cmbFieldVillage.Text, cmbFieldID.Text, cmbFieldCropType.Text, cmbCropName.Text, cmbFieldArea.Text, txtOut.Text);
string msg = "";
if (!summary.Summary(out msg))
{
XtraMessageBox.Show(msg, "提示信息", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
else
{
if (DialogResult.OK == XtraMessageBox.Show("汇总完成！", "提示信息", MessageBoxButtons.OKCancel, MessageBoxIcon.Question))
{
//MapAPI.AddShpFileToMap(txtOut.Text);
BLL.EnviVars.instance.MapControl.Refresh();
}
}
}
catch (Exception ex)
{
XtraMessageBox.Show(ex.Message);
}
finally
{
frmWait.Close();
XtraMessageBox.Show("汇总完成！");
_pMapControl.Refresh();
_pTOCControl.Update();
}
}
/// <summary>
/// Computes the log-normal distribution that best fits the given sample.
/// </summary>
/// <param name="sample">The sample to fit.</param>
/// <returns>The best fit parameters.</returns>
/// <remarks>
/// <para>The returned fit parameters are the μ (<see cref="Mu"/>) and σ (<see cref="Sigma"/>) parameters, in that order.
/// These are the same parameters, in the same order, that are required by the <see cref="LognormalDistribution(double,double)"/> constructor to
/// specify a new log-normal distribution.</para>
/// </remarks>
/// <exception cref="ArgumentNullException"><paramref name="sample"/> is null.</exception>
/// <exception cref="InsufficientDataException"><paramref name="sample"/> contains fewer than three values.</exception>
/// <exception cref="InvalidOperationException"><paramref name="sample"/> contains non-positive values.</exception>
public static FitResult FitToSample(Sample sample)
{
if (sample == null)
{
throw new ArgumentNullException(nameof(sample));
}
if (sample.Count < 3)
{
throw new InsufficientDataException();
}
// Writing out the log likelyhood from p(x), taking its
// derivatives wrt mu and sigma, and setting them equal
// to zero to find the minimizing values, you find that
// the results of the normal fit are reproduced exactly
// with x -> log x, i.e.
// \mu = < \log x >, \sigma^2 = < (\log x - \mu)^2 >
// do a one-pass computation of these quantities
SampleSummary summary = new SampleSummary();
foreach (double value in sample)
{
if (value <= 0.0)
{
throw new InvalidOperationException();
}
summary.Add(Math.Log(value));
}
// the second derivatives are also just as in the normal
// case, including the vanishing of the mixed derivative
// this makes direct inversion trivial
SymmetricMatrix C = new SymmetricMatrix(2);
C[0, 0] = summary.Variance / summary.Count;
C[1, 1] = summary.Variance / summary.Count / 2;
// test the fit
ContinuousDistribution d = new LognormalDistribution(summary.Mean, Math.Sqrt(summary.Variance));
TestResult r = sample.KolmogorovSmirnovTest(d);
return(new FitResult(new double[] { summary.Mean, Math.Sqrt(summary.Variance) }, C, r));
}
public async Task <ListResponse <SampleSummary> > GetSamplesForLabelAsync(string labelId, string contentType, EntityHeader org, EntityHeader user, ListRequest request)
{
var label = await _labelRepo.GetLabelAsync(labelId);
await AuthorizeAsync(label, AuthorizeResult.AuthorizeActions.Read, user, org);
var samples = await _sampleLabelRepo.GetSamplesForLabelAsync(labelId, contentType, request);
return(new ListResponse <SampleSummary>()
{
Model = samples.Model.Select(smp => SampleSummary.FromSampleLabel(smp)),
NextPartitionKey = samples.NextPartitionKey,
NextRowKey = samples.NextRowKey,
PageCount = samples.PageCount,
PageIndex = samples.PageIndex,
PageSize = samples.PageSize
});
}
