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ctSkennertonctSkennerton
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Add in some more messages to PMStatisticalSample (#200)
* Create Math-Statistics package and add harmonic mean geometric mean and mode. * Refactor harmonicMean to use a guard clause. Also add in a test to make sure that passing in a negative number causes an error. * change the implementation of geometricMean. * Add in guard clause to prevent use with negative numbers as the behaviour is not well defined. * Prevent a potential float under/overflow by summing the logarithms as suggested by Nicolas Cellier. * Convert extension methods into new class: PMStatisticalSample * Add in aliases in PMStatisticalSample to the basic methods in Collection. Adds in average, mean, median, stdev, variance messages * Add in shortcut class methods for mean, median, stdev, and variance * Change aCollection to aSample
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‎src/Math-Statistics/PMStatisticalSample.class.st

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@@ -36,36 +36,70 @@ Class {
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}
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{ #category : #information }
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PMStatisticalSample class >> geometricMean: aCollection [
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PMStatisticalSample class >> geometricMean: aSample [
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"Calculate the geometric mean of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aCollection) geometricMean
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^ (self newFrom: aSample) geometricMean
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]
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{ #category : #information }
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PMStatisticalSample class >> harmonicMean: aCollection [
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PMStatisticalSample class >> harmonicMean: aSample [
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"Calculate the harmonic mean of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aCollection) harmonicMean
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^ (self newFrom: aSample) harmonicMean
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]
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{ #category : #information }
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PMStatisticalSample class >> mode: aCollection [
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PMStatisticalSample class >> mean: aSample [
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"Calculate the mean of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aSample) mean
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]
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{ #category : #information }
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PMStatisticalSample class >> median: aSample [
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"Calculate the median of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aSample) median
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]
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{ #category : #information }
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PMStatisticalSample class >> mode: aSample [
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"Calculate the mode of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aCollection) mode
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^ (self newFrom: aSample) mode
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]
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{ #category : #'instance creation' }
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PMStatisticalSample class >> newFrom: aCollection [
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PMStatisticalSample class >> newFrom: aSample [
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"Create a new PMStatisticalSample with aCollection as the data"
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| ss |
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ss := self new.
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ss data: aCollection.
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ss data: aSample.
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^ ss
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]
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{ #category : #information }
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PMStatisticalSample class >> stdev: aSample [
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"Calculate the standard deviation of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aSample) stdev
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]
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{ #category : #information }
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PMStatisticalSample class >> variance: aSample [
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"Calculate the variance of a collection. This shortcut method will create a new instance of PMStatisticalSample and return the desired metric."
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^ (self newFrom: aSample) variance
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]
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{ #category : #information }
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PMStatisticalSample >> average [
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"An alias for the arithmetic mean of the sample"
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^ data average
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]
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{ #category : #accessing }
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PMStatisticalSample >> data [
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"Get the collection that this StatisticalSample is calculated against"
@@ -74,23 +108,23 @@ PMStatisticalSample >> data [
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]
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{ #category : #accessing }
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PMStatisticalSample >> data: aCollection [
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PMStatisticalSample >> data: aSample [
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"Set the collection of data points that statistical samples will be made against"
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data := aCollection
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data := aSample
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]
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{ #category : #information }
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PMStatisticalSample >> geometricMean [
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"Answer with the geometric mean of the collection"
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"(StatisticalSample new data: #(1 1 2 3 4 5 5 6 6 7 8 9)) geometricMean >>> 3.8583980015011217"
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"(PMStatisticalSample new data: #(1 1 2 3 4 5 5 6 6 7 8 9)) geometricMean >>> 3.8583980015011217"
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"(StatisticalSample new data: { 4. 1. 1 / 32}) geometricMean >>> 0.49999999999999994"
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"(PMStatisticalSample new data: { 4. 1. 1 / 32}) geometricMean >>> 0.49999999999999994"
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"(StatisticalSample new data: #(3.14 1 4.56 0.333)) geometricMean >>> 1.4776945822943937"
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"(PMStatisticalSample new data: #(3.14 1 4.56 0.333)) geometricMean >>> 1.4776945822943937"
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"(StatisticalSample new data: {1/3. 2/3. 8/3. 16/3}) geometricMean >>> 1.3333333333333335"
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"(PMStatisticalSample new data: {1/3. 2/3. 8/3. 16/3}) geometricMean >>> 1.3333333333333335"
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data
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detect: [ :i | i <= 0 ]
@@ -108,7 +142,7 @@ PMStatisticalSample >> geometricMean [
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PMStatisticalSample >> harmonicMean [
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"Answer with the harmonic mean of the data."
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"(StatisticalSample new data: #(2.5 3 10)) harmonicMean >>> 3.6"
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"(PMStatisticalSample new data: #(2.5 3 10)) harmonicMean >>> 3.6"
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| sum |
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data
@@ -120,16 +154,45 @@ PMStatisticalSample >> harmonicMean [
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^ data size / sum
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]
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{ #category : #information }
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PMStatisticalSample >> mean [
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"answers with the arithmetic mean of the sample."
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"(PMStatisticalSample new data: #(1 2 2 2 3 4 5)) mean >>> (19/7)"
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"(PMStatisticalSample new data: #(5 5 1 1 2 3 4)) mean >>> 3"
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^ self average
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]
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{ #category : #information }
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PMStatisticalSample >> median [
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"The middle value of a statistical sample."
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^ data median
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]
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{ #category : #information }
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PMStatisticalSample >> mode [
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"answers with the most common value in a collection.
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If there are values that are equally common then the one that is
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smallest is returned."
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"(StatisticalSample new data: #(1 2 2 2 3 4 5)) mode >>> 2"
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"(PMStatisticalSample new data: #(1 2 2 2 3 4 5)) mode >>> 2"
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"(StatisticalSample new data: #(5 5 1 1 2 3 4)) mode >>> 1"
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"(PMStatisticalSample new data: #(5 5 1 1 2 3 4)) mode >>> 1"
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^ (data asBag sortedCounts at: 1) value
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]
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{ #category : #information }
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PMStatisticalSample >> stdev [
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"Answers with the standard deviation of the statistical sample"
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^ data stdev
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]
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{ #category : #information }
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PMStatisticalSample >> variance [
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"Answers with the variance of the statistical sample."
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^ self stdev squared
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]

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