--- a/trafficintelligence/utils.py	Thu Jan 30 10:17:07 2020 -0500
+++ b/trafficintelligence/utils.py	Thu Jan 30 10:18:50 2020 -0500
@@ -12,7 +12,7 @@
 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, float as npfloat
-from numpy.random import permutation as nppermutation
+from numpy.random import random_sample, permutation as nppermutation
 from pandas import DataFrame, concat
 import matplotlib.pyplot as plt
 
@@ -305,6 +305,10 @@
 #             smoothed[i] = 0
 #     return smoothed
 
+def generateData(nrows, nvariables, scale):
+    x = random_sample(nrows*nvariables).reshape(nrows,nvariables)*scale
+    return DataFrame(x, columns=['x{}'.format(i+1) for i in range(nvariables)])
+
 def kernelSmoothing(x, X, Y, weightFunc, halfwidth):
     '''Returns the smoothed estimate of (X,Y) at x
     Sum_x weight(sample_x,x) * y(x)'''