def tansey_linear_regression(points):
"""
to be commented.
@param points is a list of tuples (x,y) of float values.
@return intercept,slope
"""
if len(points)==0:
return 0.
sumOfXSq = 0.
sumCodeviates = 0.
n = len(points)
for x,y in points:
sumCodeviates += (x*y)
sumOfXSq += (x*x)
sum_x = central.fsum( [x for x,y in points] )
sum_y = central.fsum( [y for x,y in points] )
mean_x = central.fmean( [x for x,y in points] )
mean_y = central.fmean( [y for x,y in points] )
ssx = sumOfXSq - ((sum_x*sum_x) / n)
sco = sumCodeviates - ((sum_x * sum_y) / n)
b = mean_y - ((sco / ssx) * mean_x)
m = sco / ssx
return b, m
def rPVI(items):
"""
Calculates the Raw Pairwise Variability Index
@param items (list) list of data values
@return (float)
"""
if len(items) < 2:
return 0.0
n = len(items) - 1
sumd = central.fsum([math.fabs(items[i] - items[i + 1]) for i in range(n)])
return sumd / n
def lvariance(items):
"""
Calculates the variance of the data values,
using N for the denominator.
The variance is a measure of dispersion near the mean.
@param items (list) list of data values
@return (float)
"""
if len(items) < 2:
return 0.0
mn = central.fmean(items)
return central.fsum(pow(i - mn, 2) for i in items) / (len(items))
