CachedHistogram

class CachedHistogram(val cacheSize: Int = 512, name: String? = null) : AbstractStatistic, HistogramIfc

Creates a dynamically configured histogram based on an observed cache. If the amount of data observed is less than cache size and greater than or equal to 2, the returned histogram will be configured on whatever data was available in the cache. Thus, bin settings may change as more data is collected until the cache is full. Once the cache is full the returned histogram is permanently configured based on all data in the cache. The default cache size cacheSize is 512 observations.

Constructors

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constructor(data: DoubleArray, cacheSize: Int = 512, name: String? = null)

If the size of the data array is less than the cache size and greater than or equal to 2, the created histogram will be configured based on whatever data was supplied. However, the bins may change if additional data is collected until the cache is full. Once the cache is full, the returned histogram is permanently configured based on the defined cache size cacheSize.

constructor(cacheSize: Int = 512, name: String? = null)

Properties

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Fills up an array with the statistics defined by this interface statistics0 = getCount() statistics1 = getAverage() statistics2 = getStandardDeviation() statistics3 = getStandardError() statistics4 = getHalfWidth() statistics5 = getConfidenceLevel() statistics6 = getMin() statistics7 = getMax() statistics8 = getSum() statistics9 = getVariance() statistics10 = getDeviationSumOfSquares() statistics11 = getLastValue() statistics12 = getKurtosis() statistics13 = getSkewness() statistics14 = getLag1Covariance() statistics15 = getLag1Correlation() statistics16 = getVonNeumannLag1TestStatistic() statistics17 = getNumberMissing()

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open val asStrings: List<String>

Returns the values of all the statistics as a list of strings The name is the first string

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open override val average: Double

Gets the unweighted average of the observations.

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open override val binArray: Array<HistogramBin>

Returns an array of Bins based on the current state of the histogram

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open override val binCounts: DoubleArray
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open override val bins: List<HistogramBin>

Returns a List of Bins based on the current state of the histogram

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open override val breakPoints: DoubleArray
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val cacheSize: Int = 512
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A confidence interval for the mean based on the confidence level

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open override var confidenceLevel: Double

Holds the confidence coefficient for the statistic

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open override val count: Double

Gets the count of the number of the observations.

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open val csvHeader: List<String>

Gets the CSV header values as a list of strings

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open override val csvStatistic: String
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open override val csvStatisticHeader: String

The header string for the CVS representation

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Returns a histogram configured with break points based on the cached observed data. If the amount of data observed is less than cache size and greater than or equal to 2, the returned histogram was configured on whatever data was available in the cache. Thus, bin tabulation may change as more data is collected until the cache is full. Then the returned histogram is permanently configured based on all data in the cache.

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A simple estimate of the "density" function for each bin using bin fraction/bin width values for each bin The bin width must be constant across the bins and not equal to 0.0

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open override val deviationSumOfSquares: Double

Gets the sum of squares of the deviations from the average This is the numerator in the classic sample variance formula

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open override val emitter: Emitter<Double>
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open override val firstBinLowerLimit: Double

The first bin's lower limit

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open val halfWidth: Double

Gets the confidence interval half-width. Simply the standard error times the confidence coefficient

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open override val id: Int
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open override val kurtosis: Double

Gets the kurtosis of the data

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open override var label: String?
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open override val lag1Correlation: Double

Gets the lag-1 generate correlation of the unweighted observations. Note: See Box, Jenkins, Reinsel, Time Series Analysis, 3rd edition, Prentice-Hall, pg 31

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open override val lag1Covariance: Double

Gets the lag-1 generate covariance of the unweighted observations. Note: See Box, Jenkins, Reinsel, Time Series Analysis, 3rd edition, Prentice-Hall, pg 31

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open override val lastBinUpperLimit: Double

The last bin's upper limit

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open override var lastValue: Double
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open override val max: Double

Gets the maximum of the observations.

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open override val min: Double

Gets the minimum of the observations.

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open override val name: String
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open override val negativeCount: Double

Counts the number of observations that were negative, strictly less than zero.

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open override val numberBins: Int
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open override var numberMissing: Double

Used to count the number of missing data points presented When a data point having the value of (Double.NaN, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY) are presented it is excluded from the summary statistics and the number of missing points is noted. Implementers of subclasses are responsible for properly collecting this value and resetting this value.

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open override val overFlowCount: Double

The number of observations that fell past the last bin's upper limit

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Counts the number of observations that were positive, strictly greater than zero.

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Returns the relative error: getStandardError() / getAverage()

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Returns the relative width of the default confidence interval: 2.0 * getHalfWidth() / getAverage()

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open override val skewness: Double

Gets the skewness of the data

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Gets the sample standard deviation of the observations. Simply the square root of variance

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open override val standardError: Double

Gets the standard error of the observations. Simply the generate standard deviation divided by the square root of the number of observations

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Fills the map with the values of the statistics. Key is statistic label and value is the value of the statistic. The keys are: "Count" "Average" "Standard Deviation" "Standard Error" "Half-width" "Confidence Level" "Lower Limit" "Upper Limit" "Minimum" "Maximum" "Sum" "Variance" "Deviation Sum of Squares" "Kurtosis" "Skewness" "Lag 1 Covariance" "Lag 1 Correlation" "Von Neumann Lag 1 Test Statistic" "Number of missing observations"

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open override val sum: Double

Gets the sum of the observations.

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open override val totalCount: Double

Total number of observations collected including overflow and underflow

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open override val underFlowCount: Double

The number of observations that fell below the first bin's lower limit

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open override var value: Double
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open override val variance: Double

Gets the sample variance of the observations.

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Gets the Von Neumann Lag 1 test statistic for checking the hypothesis that the data are uncorrelated Note: See Handbook of Simulation, Jerry Banks editor, McGraw-Hill, pg 253.

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Returns the asymptotic p-value for the Von Nueumann Lag-1 Test Statistic:

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open val width: Double
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open override val zeroCount: Double

Counts the number of observations that were exactly zero.

Functions

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open fun asString(): String
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open override fun attachObserver(observer: ObserverIfc<Double>)

Allows the adding (attaching) of an observer to the observable

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open override fun bin(x: Double): HistogramBin

The bin that x falls in. The bin is a copy. It will not reflect observations collected after this call.

open override fun bin(binNum: Int): HistogramBin

Returns an instance of a Bin for the supplied bin number The bin does not reflect changes to the histogram after this call. May throw IndexOutOfBoundsException

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open override fun binCount(x: Double): Double

Returns the current bin count for the bin associated with x

open override fun binCount(binNum: Int): Double

Returns the bin count for the indicated bin

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open override fun binFraction(x: Double): Double

Returns the fraction of the data relative to those tabulated in the bins for the bin number associated with the x

open override fun binFraction(binNum: Int): Double

Returns the fraction of the data relative to those tabulated in the bins for the supplied bin number

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open override fun binNumber(x: Double): Int

Bins are numbered starting at 1 through the number of bins

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Returns the probability for each bin of the histogram based on a continuous interval interpretation of the bin . The distribution, cdf must implement the ContinuousDistributionIfc interface

Returns the probability for each bin of the histogram based on an open integer range interpretation of the bin . The discrete distribution, discreteCDF must implement the ProbInRangeIfc interface

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open override fun collect(obs: Double)

Collect on the supplied value. Double.NaN, Double.NEGATIVE_INFINITY, and Double.POSITIVE_INFINITY values are counted as missing. Null values are not permitted.

open fun collect(obs: Boolean)

Collects on the boolean value true = 1.0, false = 0.0

open fun collect(observations: BooleanArray)
open fun collect(observations: DoubleArray)
open fun collect(observations: IntArray)
open fun collect(observations: LongArray)

Collects on the values in the supplied array.

open fun collect(fn: () -> Double)

Collect on the double value return by the function

open fun collect(obs: Int)

Collects on the Int value

open fun collect(obs: Long)

Collects on the Long value

open fun collect(observations: Collection<Double>)

Collects on all the values in the supplied collection.

open fun collect(v: GetValueIfc)

Collects on the values returned by the supplied GetValueIfc

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open operator override fun compareTo(other: AbstractStatistic): Int

Returns a negative integer, zero, or a positive integer if this object is less than, equal to, or greater than the specified object.

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A confidence interval for the mean based on the confidence level

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open override fun copyOfAsStatistic(): Statistic

Return a copy of the information as an instance of a statistic

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open override fun countObservers(): Int

Returns how many observers are currently attached to the observable

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open override fun cumulativeBinCount(x: Double): Double

Returns the cumulative count of all bins up to and including the bin containing the value x

open override fun cumulativeBinCount(binNum: Int): Double

Returns the cumulative count of all the bins up to and including the indicated bin number

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open override fun cumulativeBinFraction(x: Double): Double

Returns the cumulative fraction of the data up to and including the bin containing the value of x

open override fun cumulativeBinFraction(binNum: Int): Double

Returns the cumulative fraction of the data up to and including the indicated bin number

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open override fun cumulativeCount(x: Double): Double

Returns the cumulative count of all the data (including underflow and overflow) for all bins up to and including the bin containing x

open override fun cumulativeCount(binNum: Int): Double

Returns the cumulative count of all the data (including underflow and overflow) up to and including the indicated bin

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open override fun cumulativeFraction(x: Double): Double

Returns the cumulative fraction of all the data up to an including the bin containing the value x, (includes over and under flow)

open override fun cumulativeFraction(binNum: Int): Double

Returns the cumulative fraction of all the data up to and including the supplied bin (includes over and under flow)

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open override fun detachAllObservers()

Detaches all the observers from the observable

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open override fun detachObserver(observer: ObserverIfc<Double>)

Allows the deletion (removing) of an observer from the observable

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open override fun estimate(): Double
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Returns the expected count for each bin of the histogram based on a continuous interval interpretation of the bin . The distribution, cdf must implement the ContinuousDistributionIfc interface

open fun expectedCounts(discreteCDF: ProbInRangeIfc): DoubleArray

Returns the expected count for each bin of the histogram based on a continuous interval interpretation of the bin . The discrete distribution, discreteCDF must implement the ProbInRangeIfc interface

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open override fun findBin(x: Double): HistogramBin
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open override fun halfWidth(level: Double): Double

Gets the confidence interval half-width. Simply the standard error times the confidence coefficient as determined by an appropriate sampling distribution

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The data of the histogram bins

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open fun histogramPlot(proportions: Boolean = true): HistogramPlot

Creates a plot for the histogram. The parameter, proportions indicates whether proportions (true) or frequencies (false) will be shown on the plot. The default is true.

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open override fun isAttached(observer: ObserverIfc<Double>): Boolean

Returns true if the observer is already attached

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open override fun leadingDigitRule(multiplier: Double): Int

Computes the right most meaningful digit according to (int)Math.floor(Math.log10(a*getStandardError())) See doi 10.1287.opre.1080.0529 by Song and Schmeiser

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fun <T> Observable<T>.observe(block: (T?) -> Unit)
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open fun relativeWidth(level: Double): Double

Returns the relative width of the level of the confidence interval: 2.0 * getHalfWidth(level) / getAverage()

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open override fun reset()

After reset, it will be as if no data had been observed.

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open fun statisticData(level: Double = 0.95): StatisticData

Returns a data class holding the statistical data with the confidence interval specified by the given level.

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open fun statisticDataDb(level: Double = 0.95, context: String? = null, subject: String? = null, tableName: String = "tblStatistic"): StatisticDataDb

Returns a data class holding the statistical data with the confidence interval specified by the given level. The class is suitable for inserting into a database table.

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Converts the histogram bin data into a dataframe representation

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fun StatisticIfc.toStatDataFrame(valueLabel: String = "Value"): DataFrame<StatSchema>

Converts a statistic to a data frame with two columns. The first column holds the names of the statistics and the second column holds the values. The valueLabel can be used to provide a column name for the value columns. By default, it is "Value".

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open override fun toString(): String

Returns a String representation of the Statistic

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open fun width(level: Double): Double