// Update is called once per frame
void Update()
{
for (int i = 1024, j = 0; i < fakeSize; i++, j++)
{
lastsamples[j] = lastsamples[i];
}
for (int i = 0, j = fakeSize - 1024; j < fakeSize; i++, j++)
{
lastsamples[j] = samples[i];
}
MasterInput.RetrieveWaveform(FilterType.Bypass, samples);
for (int i = 0; i < lastsamples.Length; i++)
{
freq[i] = new Complex(lastsamples[i], 0);
}
for (int i = 0; i < samples.Length; i++)
{
freq[lastsamples.Length + i] = new Complex(samples[i], 0);
}
for (int i = 0; i < samples.Length; i++)
{
Debug.DrawLine(new Vector3(i, 0), new Vector3(i, samples[i] * 20), Color.cyan);
}
FFT.CalculateFFT(freq, false);
for (int i = 0; i < freq.Length / 2; i++) // plot only the first half
{
// multiply the magnitude of each value by 2
Debug.DrawLine(new Vector3(i, 200), new Vector3(i, 200 + (float)freq[i].magnitude * 200), Color.white);
}
List <FreqData> maximas = new List <FreqData>();
for (int i = imin; i < imax; i++)
{
if (freq[i].magnitude > noteThreshold)
{
if (freq[i].magnitude > freq[i - 1].magnitude && freq[i].magnitude > freq[i + 1].magnitude)
{
FreqData temp = new FreqData();
temp.location = i;
temp.mag = freq[i].magnitude;
maximas.Add(temp);
}
}
}
if (maximas.Count > 0)
{
maximas.Sort((s1, s2) => s1.mag.CompareTo(s2.mag));
float maxFreq = maximas[0].location * freqStep;
int closest = (int)FreqToNumber(maxFreq);
float closestFreq = NumberToFreq(closest);
ColorHSV colorA = Colors[closest % 12], colorB;
if (maxFreq - closestFreq > 0)
{
colorB = Colors[(closest + 1) % 12];
}
else
{
colorB = Colors[(closest - 1) % 12];
}
var mainModule = objectToColorChange.main;
Color x = ColorHSV.Lerp(colorA, colorB, Mathf.Abs(maxFreq - closestFreq) / maxFreq).ToColor();
mainModule.startColor = x;
}
}
protected override double[][] TrainWeights()
{
var dim = DataDim;
var cnt = DataCount;
var alp = Alpha;
var classes = Classes;
var cTotals = new double[classes.Length];
var idfFreqs = new int[dim];
var weights = new double[classes.Length][];
foreach (var cls in classes)
{
weights[cls.Value] = new double[dim];
}
var freqDatas = new FreqData[dim][];
for (int i = 0; i < dim; i++)
{
freqDatas[i] = new FreqData[cnt];
}
// TF transform
int idx = -1;
foreach (var doc in TrainingSample)
{
var text = doc.Key;
var cls = doc.Value;
bool isEmpty;
var data = ExtractFeatureVector(text, out isEmpty);
if (isEmpty)
{
continue;
}
idx++;
m_TFWeightingScheme.Reset();
for (int i = 0; i < dim; i++)
{
var f = data[i];
var tf = m_TFWeightingScheme.GetFrequency(data, i);
freqDatas[i][idx] = new FreqData(tf, cls);
if (f > 0)
{
idfFreqs[i] += 1;
}
}
}
cnt = idx + 1;
// IDF transform
var idfWeights = IDFWeightingScheme.GetWeights(dim, idfFreqs);
for (var i = 0; i < dim; i++)
{
var idf = idfWeights[i];
var freqData = freqDatas[i];
for (var j = 0; j < cnt; j++)
{
var fData = freqData[j];
var fValue = fData.Value * idf;
var cls = fData.Class;
weights[cls.Value][i] += fValue;
cTotals[cls.Value] += fValue;
}
}
// calculate weights
foreach (var cls in classes)
{
var ws = weights[cls.Value];
var t = cTotals[cls.Value];
if (UseSmoothing)
{
t += alp * dim;
}
for (int i = 0; i < dim; i++)
{
var w = ws[i];
if (UseSmoothing)
{
w += alp;
}
ws[i] = Math.Log(w / t);
}
}
return(weights);
}
protected override double[][] TrainWeights()
{
var dim = DataDim;
var cnt = DataCount;
var alp = Alpha;
var classes = Classes;
var idfFreqs = new int[dim];
var cTotals = new double[classes.Length];
var weights = new double[Classes.Length][];
foreach (var cls in Classes)
{
weights[cls.Value] = new double[dim];
}
var freqDatas = new FreqData[dim][];
for (int i = 0; i < dim; i++)
{
freqDatas[i] = new FreqData[cnt];
}
// TF transform
int idx = -1;
foreach (var doc in TrainingSample)
{
var text = doc.Key;
var cls = doc.Value;
bool isEmpty;
var data = ExtractFeatureVector(text, out isEmpty);
if (isEmpty)
{
continue;
}
idx++;
TFWeightingScheme.Reset();
for (int i = 0; i < dim; i++)
{
var f = data[i];
var tf = TFWeightingScheme.GetFrequency(data, i);
freqDatas[i][idx] = new FreqData(tf, cls);
if (f > 0)
{
idfFreqs[i] += 1;
}
}
}
cnt = idx + 1;
// IDF transform
var idfWeights = IDFWeightingScheme.GetWeights(dim, idfFreqs);
for (var i = 0; i < dim; i++)
{
var idf = idfWeights[i];
var freqData = freqDatas[i];
for (var j = 0; j < cnt; j++)
{
var fData = freqData[j];
freqData[j] = new FreqData(fData.Value * idf, fData.Class);
}
}
// length norm
for (var j = 0; j < cnt; j++)
{
var norm = 0.0D;
for (var i = 0; i < dim; i++)
{
var fData = freqDatas[i][j];
norm += (fData.Value * fData.Value);
}
norm = Math.Sqrt(norm);
for (var i = 0; i < dim; i++)
{
var fData = freqDatas[i][j];
freqDatas[i][j] = new FreqData(fData.Value / norm, fData.Class);
}
}
// complements
for (var j = 0; j < cnt; j++)
{
foreach (var cCls in Classes)
{
var ws = weights[cCls.Value];
for (var i = 0; i < dim; i++)
{
var fData = freqDatas[i][j];
var f = fData.Value;
var cls = fData.Class;
if (cCls.Equals(cls))
{
break;
}
ws[i] += f;
cTotals[cCls.Value] += f;
}
}
}
// take logarithm
foreach (var cls in Classes)
{
var ws = weights[cls.Value];
var t = cTotals[cls.Value];
if (UseSmoothing)
{
t += alp * dim;
}
for (int i = 0; i < dim; i++)
{
var w = ws[i];
if (UseSmoothing)
{
w += alp;
}
ws[i] = -Math.Log(w / t);
}
}
// weight normalization
foreach (var cls in Classes)
{
cTotals[cls.Value] = 0;
var ws = weights[cls.Value];
for (var i = 0; i < dim; i++)
{
cTotals[cls.Value] += Math.Abs(ws[i]);
}
}
foreach (var cls in Classes)
{
var t = cTotals[cls.Value];
var ws = weights[cls.Value];
for (var i = 0; i < dim; i++)
{
ws[i] = ws[i] / t;
}
}
return(weights);
}
