# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1580397530 18000
# Node ID 2682b4696cdf0e39be8a83eb1b4e82253c9e299c
# Parent  b358bed29ab44dd2b5fd1a373032c5366184f73a# Parent  91faf679e8986f38e61bba4fc27cf7ed3f98833c
merged

diff -r b358bed29ab4 -r 2682b4696cdf trafficintelligence/moving.py
diff -r b358bed29ab4 -r 2682b4696cdf trafficintelligence/utils.py
--- 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)'''