--- a/trafficintelligence/utils.py	Tue Jun 19 10:04:52 2018 -0400
+++ b/trafficintelligence/utils.py	Tue Jun 19 17:07:35 2018 -0400
@@ -10,7 +10,7 @@
 from scipy.stats import rv_continuous, kruskal, shapiro, lognorm, norm, t
 from scipy.spatial import distance
 from scipy.sparse import dok_matrix
-from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log, polyfit
+from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log, polyfit, float as npfloat
 from numpy.random import permutation as nppermutation
 from pandas import DataFrame, concat
 import matplotlib.pyplot as plt
@@ -135,11 +135,12 @@
     '''Returns the Chi2 statistics'''
     return sum([((e-o)*(e-o))/float(e) for e, o in zip(expected, observed)])
 
-class generateDistribution(rv_continuous):
-    def __init__(self, values, probabilities):
+class EmpiricalContinuousDistribution(rv_continuous):
+    def __init__(self, values, probabilities, **kwargs):
         '''The values (and corresponding probabilities) are supposed to be sorted by value
         for v, p in zip(values, probabilities): P(X<=v)=p'''
         assert probabilities[0]==0
+        super(EmpiricalContinuousDistribution, self).__init__(**kwargs)
         self.values = values
         self.probabilities = probabilities
     
@@ -153,7 +154,7 @@
             if i == len(self.values)-1:
                 return self.probabilities[-1]
             else:
-                return (self.probabilities[i+1]-self.probabilities[i])/(self.values[i+1]-self.values[i])
+                return self.probabilities[i]+(x-self.values[i])*float(self.probabilities[i+1]-self.probabilities[i])/float(self.values[i+1]-self.values[i])
 
 class DistributionSample(object):
     def nSamples(self):
@@ -164,7 +165,7 @@
     xaxis = sorted(sample)
     counts = arange(1,len(sample)+1) # dtype = float
     if normalized:
-        counts /= float(len(sample))
+        counts = counts.astype(float)/float(len(sample))
     return xaxis, counts
 
 class DiscreteDistributionSample(DistributionSample):