the f-measure if we just guess as negativ the first numleft and guess as poitive the last numright
437819
public double fmeasure(int numleft, int numright) {
    int tp = 0, fp = 0, fn = 0;
    tp = numpositive[numright];
    fp = numright - tp;
    fn = numleft - numnegative[numleft];
    return f1(tp, fp, fn);
  }
983863
def fpr(y, z):
    
    tp, tn, fp, fn = contingency_table(y, z)
    return fp / (fp + tn)
86908
def l1_error(true, pred):
  """"""
  return tf.reduce_sum(tf.abs(true - pred)) / tf.to_float(tf.size(pred))
324537
function decodeSample_(nibble) {
  /** @type {number} */
  let difference = 0;
  if (nibble & 4) {
    difference += decoderStep_;
  }
  if (nibble & 2) {
    difference += decoderStep_ >> 1;
  }
  if (nibble & 1) {
    difference += decoderStep_ >> 2;
  }
  difference += decoderStep_ >> 3;
  if (nibble & 8) {
    difference = -difference;
  }
  decoderPredicted_ += difference;
  if (decoderPredicted_ > 32767) {
    decoderPredicted_ = 32767;
  } else if (decoderPredicted_ < -32767) {
    decoderPredicted_ = -32767;
  }
  updateDecoder_(nibble);
  return decoderPredicted_;
}
1146656
def logit_(x:Tensor)->Tensor:
    ""
    x.clamp_(1e-7, 1-1e-7)
    return (x.reciprocal_().sub_(1)).log_().neg_()
720070
function defaultComp(a,b) {
		var as = '' + a,
			bs = '' + b;

		if (as < bs) {
			return -1;
		} else if (as > bs) {
			return 1;
		} else {
			return 0;
		}
	}
875400
function NeuQuant(pixels, samplefac) {
  var network; // int[netsize][4]
  var netindex; // for network lookup - really 256

  // bias and freq arrays for learning
  var bias;
  var freq;
  var radpower;

  /*
    Private Method: init

    sets up arrays
  */
  function init() {
    network = [];
    netindex = new Int32Array(256);
    bias = new Int32Array(netsize);
    freq = new Int32Array(netsize);
    radpower = new Int32Array(netsize >> 3);

    var i, v;
    for (i = 0; i < netsize; i++) {
      v = (i << (netbiasshift + 8)) / netsize;
      network[i] = new Float64Array([v, v, v, 0]);
      //network[i] = [v, v, v, 0]
      freq[i] = intbias / netsize;
      bias[i] = 0;
    }
  }

  /*
    Private Method: unbiasnet

    unbiases network to give byte values 0..255 and record position i to prepare for sort
  */
  function unbiasnet() {
    for (var i = 0; i < netsize; i++) {
      network[i][0] >>= netbiasshift;
      network[i][1] >>= netbiasshift;
      network[i][2] >>= netbiasshift;
      network[i][3] = i; // record color number
    }
  }

  /*
    Private Method: altersingle

    moves neuron *i* towards biased (b,g,r) by factor *alpha*
  */
  function altersingle(alpha, i, b, g, r) {
    network[i][0] -= (alpha * (network[i][0] - b)) / initalpha;
    network[i][1] -= (alpha * (network[i][1] - g)) / initalpha;
    network[i][2] -= (alpha * (network[i][2] - r)) / initalpha;
  }

  /*
    Private Method: alterneigh

    moves neurons in *radius* around index *i* towards biased (b,g,r) by factor *alpha*
  */
  function alterneigh(radius, i, b, g, r) {
    var lo = Math.abs(i - radius);
    var hi = Math.min(i + radius, netsize);

    var j = i + 1;
    var k = i - 1;
    var m = 1;

    var p, a;
    while ((j < hi) || (k > lo)) {
      a = radpower[m++];

      if (j < hi) {
        p = network[j++];
        p[0] -= (a * (p[0] - b)) / alpharadbias;
        p[1] -= (a * (p[1] - g)) / alpharadbias;
        p[2] -= (a * (p[2] - r)) / alpharadbias;
      }

      if (k > lo) {
        p = network[k--];
        p[0] -= (a * (p[0] - b)) / alpharadbias;
        p[1] -= (a * (p[1] - g)) / alpharadbias;
        p[2] -= (a * (p[2] - r)) / alpharadbias;
      }
    }
  }

  /*
    Private Method: contest

    searches for biased BGR values
  */
  function contest(b, g, r) {
    /*
      finds closest neuron (min dist) and updates freq
      finds best neuron (min dist-bias) and returns position
      for frequently chosen neurons, freq[i] is high and bias[i] is negative
      bias[i] = gamma * ((1 / netsize) - freq[i])
    */

    var bestd = ~(1 << 31);
    var bestbiasd = bestd;
    var bestpos = -1;
    var bestbiaspos = bestpos;

    var i, n, dist, biasdist, betafreq;
    for (i = 0; i < netsize; i++) {
      n = network[i];

      dist = Math.abs(n[0] - b) + Math.abs(n[1] - g) + Math.abs(n[2] - r);
      if (dist < bestd) {
        bestd = dist;
        bestpos = i;
      }

      biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
      if (biasdist < bestbiasd) {
        bestbiasd = biasdist;
        bestbiaspos = i;
      }

      betafreq = (freq[i] >> betashift);
      freq[i] -= betafreq;
      bias[i] += (betafreq << gammashift);
    }

    freq[bestpos] += beta;
    bias[bestpos] -= betagamma;

    return bestbiaspos;
  }

  /*
    Private Method: inxbuild

    sorts network and builds netindex[0..255]
  */
  function inxbuild() {
    var i, j, p, q, smallpos, smallval, previouscol = 0, startpos = 0;
    for (i = 0; i < netsize; i++) {
      p = network[i];
      smallpos = i;
      smallval = p[1]; // index on g
      // find smallest in i..netsize-1
      for (j = i + 1; j < netsize; j++) {
        q = network[j];
        if (q[1] < smallval) { // index on g
          smallpos = j;
          smallval = q[1]; // index on g
        }
      }
      q = network[smallpos];
      // swap p (i) and q (smallpos) entries
      if (i != smallpos) {
        j = q[0];   q[0] = p[0];   p[0] = j;
        j = q[1];   q[1] = p[1];   p[1] = j;
        j = q[2];   q[2] = p[2];   p[2] = j;
        j = q[3];   q[3] = p[3];   p[3] = j;
      }
      // smallval entry is now in position i

      if (smallval != previouscol) {
        netindex[previouscol] = (startpos + i) >> 1;
        for (j = previouscol + 1; j < smallval; j++)
          netindex[j] = i;
        previouscol = smallval;
        startpos = i;
      }
    }
    netindex[previouscol] = (startpos + maxnetpos) >> 1;
    for (j = previouscol + 1; j < 256; j++)
      netindex[j] = maxnetpos; // really 256
  }

  /*
    Private Method: inxsearch

    searches for BGR values 0..255 and returns a color index
  */
  function inxsearch(b, g, r) {
    var a, p, dist;

    var bestd = 1000; // biggest possible dist is 256*3
    var best = -1;

    var i = netindex[g]; // index on g
    var j = i - 1; // start at netindex[g] and work outwards

    while ((i < netsize) || (j >= 0)) {
      if (i < netsize) {
        p = network[i];
        dist = p[1] - g; // inx key
        if (dist >= bestd) i = netsize; // stop iter
        else {
          i++;
          if (dist < 0) dist = -dist;
          a = p[0] - b; if (a < 0) a = -a;
          dist += a;
          if (dist < bestd) {
            a = p[2] - r; if (a < 0) a = -a;
            dist += a;
            if (dist < bestd) {
              bestd = dist;
              best = p[3];
            }
          }
        }
      }
      if (j >= 0) {
        p = network[j];
        dist = g - p[1]; // inx key - reverse dif
        if (dist >= bestd) j = -1; // stop iter
        else {
          j--;
          if (dist < 0) dist = -dist;
          a = p[0] - b; if (a < 0) a = -a;
          dist += a;
          if (dist < bestd) {
            a = p[2] - r; if (a < 0) a = -a;
            dist += a;
            if (dist < bestd) {
              bestd = dist;
              best = p[3];
            }
          }
        }
      }
    }

    return best;
  }

  /*
    Private Method: learn

    "Main Learning Loop"
  */
  function learn() {
    var i;

    var lengthcount = pixels.length;
    var alphadec = 30 + ((samplefac - 1) / 3);
    var samplepixels = lengthcount / (3 * samplefac);
    var delta = ~~(samplepixels / ncycles);
    var alpha = initalpha;
    var radius = initradius;

    var rad = radius >> radiusbiasshift;

    if (rad <= 1) rad = 0;
    for (i = 0; i < rad; i++)
      radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));

    var step;
    if (lengthcount < minpicturebytes) {
      samplefac = 1;
      step = 3;
    } else if ((lengthcount % prime1) !== 0) {
      step = 3 * prime1;
    } else if ((lengthcount % prime2) !== 0) {
      step = 3 * prime2;
    } else if ((lengthcount % prime3) !== 0)  {
      step = 3 * prime3;
    } else {
      step = 3 * prime4;
    }

    var b, g, r, j;
    var pix = 0; // current pixel

    i = 0;
    while (i < samplepixels) {
      b = (pixels[pix] & 0xff) << netbiasshift;
      g = (pixels[pix + 1] & 0xff) << netbiasshift;
      r = (pixels[pix + 2] & 0xff) << netbiasshift;

      j = contest(b, g, r);

      altersingle(alpha, j, b, g, r);
      if (rad !== 0) alterneigh(rad, j, b, g, r); // alter neighbours

      pix += step;
      if (pix >= lengthcount) pix -= lengthcount;

      i++;

      if (delta === 0) delta = 1;
      if (i % delta === 0) {
        alpha -= alpha / alphadec;
        radius -= radius / radiusdec;
        rad = radius >> radiusbiasshift;

        if (rad <= 1) rad = 0;
        for (j = 0; j < rad; j++)
          radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
      }
    }
  }

  /*
    Method: buildColormap

    1. initializes network
    2. trains it
    3. removes misconceptions
    4. builds colorindex
  */
  function buildColormap() {
    init();
    learn();
    unbiasnet();
    inxbuild();
  }
  this.buildColormap = buildColormap;

  /*
    Method: getColormap

    builds colormap from the index

    returns array in the format:

    >
    > [r, g, b, r, g, b, r, g, b, ..]
    >
  */
  function getColormap() {
    var map = [];
    var index = [];

    for (var i = 0; i < netsize; i++)
      index[network[i][3]] = i;

    var k = 0;
    for (var l = 0; l < netsize; l++) {
      var j = index[l];
      map[k++] = (network[j][0]);
      map[k++] = (network[j][1]);
      map[k++] = (network[j][2]);
    }
    return map;
  }
  this.getColormap = getColormap;

  /*
    Method: lookupRGB

    looks for the closest *r*, *g*, *b* color in the map and
    returns its index
  */
  this.lookupRGB = inxsearch;
}
402570
def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
    ""
    pred,targ = flatten_check(pred,targ)
    return F.mse_loss(pred, targ)
515373
def overall_f_measure(self):
        
        if self.overall['Nsys'] == 0 and self.empty_system_output_handling == 'zero_score':
            precision = 0

        else:
            precision = metric.precision(
                Ntp=self.overall['Ntp'],
                Nsys=self.overall['Nsys']
            )

        recall = metric.recall(
            Ntp=self.overall['Ntp'],
            Nref=self.overall['Nref']
        )

        f_measure = metric.f_measure(
            precision=precision,
            recall=recall
        )

        return {
            'f_measure': f_measure,
            'precision': precision,
            'recall': recall
        }
753585
def f(self, v, t):
        
        ret = np.zeros(8)
        ret[0] = v[4]
        ret[4] = (self.He1*self.ke1*(self.g1*self.S(v[1]-v[2]) + self.u_mean) -
                  2*self.ke1*v[4] - self.ke1*self.ke1*v[0])

        ret[1] = v[5]
        ret[5] = (self.He2*self.ke2*(self.g2*self.S(v[0]) + self.p_mean) -
                  2*self.ke2*v[5] - self.ke2*self.ke2*v[1])

        ret[2] = v[6]
        ret[6] = (self.Hi*self.ki*self.g4*self.S(v[3]) - 2*self.ki*v[6] -
                  self.ki*self.ki*v[2])

        ret[3] = v[7]
        ret[7] = (self.He3*self.ke3*self.g3*self.S(v[1]-v[2]) -
                  2*self.ke3*v[7] - self.ke3*self.ke3*v[3])
        return ret
1085094
def _guess_x_simple(self, y_desired, y_dims=None, **kwargs):
        """"""
        _, indexes = self.fmodel.dataset.nn_y(y_desired, dims=y_dims, k = 10)
        return [self.fmodel.get_x(i) for i in indexes]
530234
def p_feature_soft(self, t):
        """"""

        t[0] = SoftFeatures(t[2], t[4], line=t.lineno(1))
216664
def predict(self, u=0):
        

        # x = Fx + Bu
        self.x = dot(self.F, self.x) + dot(self.B, u)
1102744
def u_probs(self):
        """"""
        log_u = self.kernel.feature_log_prob_[self._nonmatch_class_pos()]

        return self._prob_inverse_transform(numpy.exp(log_u))
81036
def F(self, **kwargs):
        '''
        
        '''
        Eg = self.Eg_Gamma_0(**kwargs)
        Delta_SO = self.Delta_SO(**kwargs)
        Ep = self.Ep(**kwargs)
        meff = self.meff_e_Gamma_0(**kwargs)
        return (1./meff-1-(Ep*(Eg+2.*Delta_SO/3.))/(Eg*(Eg+Delta_SO)))/2
276765
function(normalizedLeft, normalizedRight) {
    if (normalizedLeft.length !== normalizedRight.length) {
        return normalizedLeft.length - normalizedRight.length;
    }

    for (var i = normalizedLeft.length - 1; i >= 0; i -= 1) {
        if (normalizedLeft[i] !== normalizedRight[i]) {
            return normalizedLeft[i] - normalizedRight[i];
        }
    }

    return 0;
}
657646
def p_negedgesig(self, p):
        ''
        p[0] = Sens(p[2], 'negedge', lineno=p.lineno(1))
        p.set_lineno(0, p.lineno(1))