smile.math.distance
Class ManhattanDistance
- java.lang.Object
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- smile.math.distance.ManhattanDistance
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public class ManhattanDistance extends java.lang.Object implements Metric<double[]>, java.io.Serializable
Manhattan distance, also known as L1 distance or L1 norm, is the sum of the (absolute) differences of their coordinates. Use getInstance() to get the standard unweighted Manhattan distance. Or create an instance with a specified weight vector. For float or double arrays, missing values (i.e. NaN) are also handled. Also support sparse arrays of which zeros are excluded to save space.- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor and Description ManhattanDistance()Constructor.ManhattanDistance(double[] weight)Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method and Description doubled(double[] x, double[] y)Manhattan distance between two arrays of type double.doubled(float[] x, float[] y)Manhattan distance between two arrays of type float.doubled(int[] x, int[] y)Manhattan distance between two arrays of type integer.java.lang.StringtoString()
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Constructor Detail
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ManhattanDistance
public ManhattanDistance()
Constructor.
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ManhattanDistance
public ManhattanDistance(double[] weight)
Constructor.- Parameters:
weight- the weight vector.
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Method Detail
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toString
public java.lang.String toString()
- Overrides:
toStringin classjava.lang.Object
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d
public double d(int[] x, int[] y)Manhattan distance between two arrays of type integer.
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d
public double d(float[] x, float[] y)Manhattan distance between two arrays of type float. NaN will be treated as missing values and will be excluded from the calculation. Let m be the number non-missing values, and n be the number of all values. The returned distance is n * d / m, where d is the distance between non-missing values.
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d
public double d(double[] x, double[] y)Manhattan distance between two arrays of type double. NaN will be treated as missing values and will be excluded from the calculation. Let m be the number non-missing values, and n be the number of all values. The returned distance is n * d / m, where d is the distance between non-missing values.
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