Mercurial > repos > yufei-luo > s_mart
view commons/core/stat/Stat.py @ 14:c79b9ae3f65f
Deleted selected files
author | m-zytnicki |
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date | Fri, 19 Apr 2013 10:13:11 -0400 |
parents | 769e306b7933 |
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import math class Stat(object): def __init__(self, lValues = []): self.reset() if lValues != []: self.fill(lValues) def __eq__(self, o): self._lValues.sort() o._lValues.sort() return self._lValues == o._lValues and round(self._sum, 6) == round(o._sum, 6) \ and round(self._sumOfSquares, 6) == round(o._sumOfSquares, 6) and self._n == self._n \ and round(self._min, 6) == round(o._min, 6) and round(self._max, 6) == round(o._max, 6) def getValuesList(self): return self._lValues def getSum(self): return self._sum def getSumOfSquares(self): return self._sumOfSquares def getValuesNumber(self): return self._n def getMin(self): return self._min def getMax(self): return self._max ## Reset all attributes # def reset(self): self._lValues = [] self._sum = 0.0 self._sumOfSquares = 0.0 self._n = 0 self._max = 0.0 self._min = 0.0 ## Add a value to Stat instance list and update attributes # # @param v float value to add # def add(self, v): self._lValues.append( float(v) ) self._sum += float(v) self._sumOfSquares += float(v) * float(v) self._n = self._n + 1 if v > self._max: self._max = float(v) if self._n == 1: self._min = float(v) elif v < self._min: self._min = float(v) ## Add a list of values to Stat instance list and update attributes # # @param lValues list of float list to add # def fill(self, lValues): for v in lValues: self.add(v) ## Get the arithmetic mean of the Stat instance list # # @return float # def mean(self): if self._n == 0: return 0 else: return self._sum / float(self._n) ## Get the variance of the sample # @note we consider a sample, not a population. So for calculation, we use n-1 # # @return float # def var(self): if self._n < 2 or self.mean() == 0.0: return 0 else: variance = self._sumOfSquares/float(self._n - 1) - self._n/float(self._n - 1) * self.mean()*self.mean() if round(variance, 10) == 0: variance = 0 return variance ## Get the standard deviation of the sample # # @return float # def sd(self): return math.sqrt( self.var() ) ## Get the coefficient of variation of the sample # # @return float # def cv(self): if self._n < 2 or self.mean() == 0.0: return 0 else: return self.sd() / self.mean() ## Get the median of the sample # # @return number or "NA" (Not available) # def median( self ): if len(self._lValues) == 0: return "NA" if len(self._lValues) == 1: return self._lValues[0] self._lValues.sort() m = int( math.ceil( len(self._lValues) / 2.0 ) ) if len(self._lValues) % 2: return self._lValues[m-1] else: return ( self._lValues[m-1] + self._lValues[m] ) / 2.0 ## Get the kurtosis (measure of whether the data are peaked or flat relative to a normal distribution, 'coef d'aplatissement ' in french)). # k = 0 -> completely flat # k = 3 -> same as normal distribution # k >> 3 -> peak # # @return float # def kurtosis(self): numerator = 0 for i in self._lValues: numerator += math.pow( i - self.mean(), 4 ) return numerator / float(self._n - 1) * self.sd() ## Prepare a string with calculations on your values # # @return string # def string(self): msg = "" msg += "n=%d" % ( self._n ) msg += " mean=%5.3f" % ( self.mean() ) msg += " var=%5.3f" % ( self.var() ) msg += " sd=%5.3f" % ( self.sd() ) msg += " min=%5.3f" % ( self.getMin() ) median = self.median() if median == "NA": msg += " med=%s" % (median) else: msg += " med=%5.3f" % (median) msg += " max=%5.3f" % ( self.getMax() ) return msg ## Print descriptive statistics # def view(self): print self.string() ## Return sorted list of values, ascending (default) or descending # # @return list # def sort( self, isReverse = False ): self._lValues.sort(reverse = isReverse) return self._lValues ## Give the quantile corresponding to the chosen percentage # # @return number # def quantile( self, percentage ): if self._n == 0: return 0 elif percentage == 1: return self.getMax() else: return self.sort()[int(self._n * percentage)] ## Prepare a string with quantile values # # @return string # def stringQuantiles( self ): return "n=%d min=%5.3f Q1=%5.3f median=%5.3f Q3=%5.3f max=%5.3f" % \ (self._n, self.quantile(0), self.quantile(0.25), self.quantile(0.5), self.quantile(0.75), self.quantile(1)) ## Print quantiles string # def viewQuantiles( self ): print self.stringQuantiles() ## Compute N50 # @return number def N50(self ): lSorted = self.sort(True) midlValues = self.getSum() / 2 cumul = 0 index = 0 while cumul < midlValues: cumul = cumul + lSorted[index] index += 1 if (index == 0): return lSorted[index] else : return lSorted[index - 1]