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add doco for array/seq helper procs

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JamesP 2015-11-12 07:03:16 +10:00
commit 41861711c8
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lib/pure/stats.nim
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@ -30,6 +30,21 @@
## - intercept ## - intercept
## - correlation ## - correlation
## ##
## Procs have been provided to calculate statistics on arrays and sequences.
##
## However, if more than a single statistical calculation is required, it is more
## efficient to push the data once to the RunningStat object, and
## call the numerous statistical procs for the RunningStat object.
##
## .. code-block:: Nim
##
## var rs: RunningStat
## rs.push(MySeqOfData)
## rs.mean()
## rs.variance()
## rs.skewness()
## rs.kurtosis()
from math import FloatClass, sqrt, pow, round from math import FloatClass, sqrt, pow, round
{.push debugger:off .} # the user does not want to trace a part {.push debugger:off .} # the user does not want to trace a part
@ -168,46 +183,55 @@ proc `+=`*(a: var RunningStat, b: RunningStat) {.inline.} =
a = a + b a = a + b
# ---------------------- standalone array/seq stats --------------------- # ---------------------- standalone array/seq stats ---------------------
proc mean*[T](x: openArray[T]): float = proc mean*[T](x: openArray[T]): float =
## computes the mean of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.mean() result = rs.mean()
proc variance*[T](x: openArray[T]): float = proc variance*[T](x: openArray[T]): float =
## computes the population variance of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.variance() result = rs.variance()
proc varianceS*[T](x: openArray[T]): float = proc varianceS*[T](x: openArray[T]): float =
## computes the sample variance of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.varianceS() result = rs.varianceS()
proc standardDeviation*[T](x: openArray[T]): float = proc standardDeviation*[T](x: openArray[T]): float =
## computes the population standardDeviation of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.standardDeviation() result = rs.standardDeviation()
proc standardDeviationS*[T](x: openArray[T]): float = proc standardDeviationS*[T](x: openArray[T]): float =
## computes the sanple standardDeviation of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.standardDeviationS() result = rs.standardDeviationS()
proc skewness*[T](x: openArray[T]): float = proc skewness*[T](x: openArray[T]): float =
## computes the population skewness of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.skewness() result = rs.skewness()
proc skewnessS*[T](x: openArray[T]): float = proc skewnessS*[T](x: openArray[T]): float =
## computes the sample skewness of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.skewnessS() result = rs.skewnessS()
proc kurtosis*[T](x: openArray[T]): float = proc kurtosis*[T](x: openArray[T]): float =
## computes the population kurtosis of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.kurtosis() result = rs.kurtosis()
proc kurtosisS*[T](x: openArray[T]): float = proc kurtosisS*[T](x: openArray[T]): float =
## computes the sample kurtosis of `x`
var rs: RunningStat var rs: RunningStat
rs.push(x) rs.push(x)
result = rs.kurtosisS() result = rs.kurtosisS()