returns the cumulative sample standard deviation and returns the square root of the sample variance.

@param expr argument
@return stddev_samp(expr)
429165
public static <T extends Number> WindowOver<T> stddevSamp(Expression<T> expr) {
        return new WindowOver<T>(expr.getType(), SQLOps.STDDEVSAMP, expr);
    }
115440
def sd(self):
        ''''''
        v = self.var()
        if len(v):
            return np.sqrt(v)
        else:
            return None
264230
public static float[] stddev (int[] values, int start, int length)
    {
        // first we need the mean
        float mean = 0f;
        for (int ii = start, end = start + length; ii < end; ii++) {
            mean += values[ii];
        }
        mean /= length;

        // next we compute the variance
        float variance = 0f;
        for (int ii = start, end = start + length; ii < end; ii++) {
            float value = values[ii] - mean;
            variance += value * value;
        }
        variance /= (length - 1);

        // the standard deviation is the square root of the variance
        return new float[] { mean, variance, (float)Math.sqrt(variance) };
    }
661080
public SDVariable squaredDifference(SDVariable x) {
        return squaredDifference(sameDiff.generateNewVarName(SquaredDifferenceOp.OP_NAME,0),x);
    }
1070657
def stddev(values, meanval=None):  #from AI: A Modern Appproach
    """"""
    if meanval == None: meanval = mean(values)
    return math.sqrt( sum([(x - meanval)**2 for x in values]) / (len(values)-1) )
479584
def sqrt(self, val, flag):
        
        if val.iszero():
            return val
        sw = self.p % 8
        if sw == 3 or sw == 7:
            res = val ** ((self.p + 1) / 4)
        elif sw == 5:
            x = val ** ((self.p + 1) / 4)
            if x == 1:
                res = val ** ((self.p + 3) / 8)
            else:
                res = (4 * val) ** ((self.p - 5) / 8) * 2 * val
        else:
            raise Exception("modsqrt non supported for (p%8)==1")

        if res.value % 2 == flag:
            return res
        else:
            return -res
253054
public double getSampleKurtosis() {
    if(!(m2 > 0) || !(n > 3)) {
      throw new ArithmeticException("Kurtosis not defined when variance is 0 or weight <= 3.0!");
    }
    final double nm1 = n - 1.;
    return (nm1 / ((n - 2.) * (n - 3.))) * (n * (n + 1) * m4 / (m2 * m2) - 3 * nm1) + 3;
  }
762433
def stdev(x):
    
    """
    from math import sqrt
    n = len(x)
    mean = sum(x) / float(n)
    std = sqrt(sum((a - mean)**2 for a in x) / float(n - 1))
    return std
1266772
def _get_stddevs(self, C, rup, shape, stddev_types):
        
        weight = self._compute_weight_std(C, rup.mag)
        std_intra = weight * C["sd1"] * np.ones(shape)
        std_inter = weight * C["sd2"] * np.ones(shape)
        
        stddevs = []
        for stddev_type in stddev_types:
            assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
            if stddev_type == const.StdDev.TOTAL:
                stddevs.append(np.sqrt(std_intra ** 2. + std_inter ** 2.))
            elif stddev_type == const.StdDev.INTRA_EVENT:
                stddevs.append(std_intra)
            elif stddev_type == const.StdDev.INTER_EVENT:
                stddevs.append(std_inter)
        return stddevs
376280
public static double stdDev(double[] a) {

    	if(a == null || a.length == 0)
    		return -1;
    	
        return Math.sqrt(varp(a));
    }
76466
public static double standardDeviation(double[] x, double mean) {
        if (x.length == 0) {
            return Double.NaN;
        }
        if (x.length == 1) {
            return 0.0;
        }
        return Math.sqrt(variance(x, mean));
    }
770564
private function _gauss($m = 0.0, $s = 1.0)
    {
        // N(0,1)
        // returns random number with normal distribution:
        //   mean = 0
        //   std dev = 1
        $x = (float)(rand() / (float)getrandmax());
        $y = (float)(rand() / (float)getrandmax());
        return ((sqrt(-2 * log($x)) * cos(2 * pi() * $y)) * $s + $m);
    }
1166730
def _get_stddevs(self, C, mag, stddev_types, num_sites):
        
        std = C['c16'] + np.zeros(num_sites)

        if mag < 7.4:
            std -= 0.07 * mag
        else:
            std -= 0.518

        # only the 'total' standard deviation is supported, therefore the
        # std is always the same for all types
        stddevs = [std for _ in stddev_types]

        return stddevs
1088641
public static function extractPluralCount($expr)
    {
        $parts = explode(';', $expr, 2);
        $nplurals = explode('=', trim($parts[0]), 2);
        if (strtolower(rtrim($nplurals[0])) != 'nplurals') {
            return 1;
        }
        if (count($nplurals) == 1) {
            return 1;
        }

        return intval($nplurals[1]);
    }
146183
public Double getVariance() {
		if (counter == 0) {
			return Double.NaN;
		} else {
			final double mean = computeMean();
			final double meanSquare = mean * mean;
			final double squareMean = ((double) squareTotal) / counter;
			return squareMean - meanSquare;
		}
	}
487021
def _get_stddevs(self, C, stddev_types, num_sites):
        
        stddevs = []
        for stddev_type in stddev_types:
            assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
            stddevs.append(np.log(10 ** C['sigma']) + np.zeros(num_sites))

        return stddevs
1027933
def stdevp(data, data_mean=None, data_variance=None):
    
    data_variance = data_variance or variance(data, data_mean)
    return sqrt(data_variance)