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smile.plot

Operators

trait Operators extends AnyRef

Data visualization operators.

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  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. def +(other: String): String
    Implicit
    This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (Operators, B)
    Implicit
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
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    ArrowAssoc
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    @inline()
  5. final def ==(arg0: Any): Boolean
    Definition Classes
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  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def boxplot(data: Array[Array[Double]], labels: Array[String]): Window

    Box plot.

    Box plot.

    data

    a data matrix of which each row will create a box plot.

    labels

    the labels for each box plot.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  8. def boxplot(data: Array[Double]*): Window

    A box plot is a convenient way of graphically depicting groups of numerical data through their five-number summaries (the smallest observation (sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation (sample maximum).

    A box plot is a convenient way of graphically depicting groups of numerical data through their five-number summaries (the smallest observation (sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation (sample maximum). A box plot may also indicate which observations, if any, might be considered outliers.

    Box plots can be useful to display differences between populations without making any assumptions of the underlying statistical distribution: they are non-parametric. The spacings between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers.

    For a data set, we construct a boxplot in the following manner:

    • Calculate the first q1, the median q2 and third quartile q3. - Calculate the interquartile range (IQR) by subtracting the first quartile from the third quartile. (q3 ? q1)
    • Construct a box above the number line bounded on the bottom by the first quartile (q1) and on the top by the third quartile (q3).
    • Indicate where the median lies inside of the box with the presence of a line dividing the box at the median value.
    • Any data observation which lies more than 1.5*IQR lower than the first quartile or 1.5IQR higher than the third quartile is considered an outlier. Indicate where the smallest value that is not an outlier is by connecting it to the box with a horizontal line or "whisker". Optionally, also mark the position of this value more clearly using a small vertical line. Likewise, connect the largest value that is not an outlier to the box by a "whisker" (and optionally mark it with another small vertical line).
    • Indicate outliers by dots.
    data

    a data matrix of which each row will create a box plot.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def contour(x: Array[Double], y: Array[Double], z: Array[Array[Double]], levels: Array[Double], palette: Array[Color]): Window

    Contour plot.

    Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.

    x

    the x coordinates of the data grid of z. Must be in ascending order.

    y

    the y coordinates of the data grid of z. Must be in ascending order.

    z

    the data matrix to create contour plot.

    levels

    the level values of contours.

    palette

    the color for each contour level.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  11. def contour(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Window

    Contour plot.

    Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.

    x

    the x coordinates of the data grid of z. Must be in ascending order.

    y

    the y coordinates of the data grid of z. Must be in ascending order.

    z

    the data matrix to create contour plot.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  12. def contour(z: Array[Array[Double]], levels: Array[Double], palette: Array[Color]): Window

    Contour plot.

    Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.

    z

    the data matrix to create contour plot.

    levels

    the level values of contours.

    palette

    the color for each contour level.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  13. def contour(z: Array[Array[Double]]): Window

    Contour plot.

    Contour plot. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot.

    z

    the data matrix to create contour plot.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  14. def dendrogram(merge: Array[Array[Int]], height: Array[Double]): Window

    A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.

    A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.

    merge

    an n-1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering. If an element j in the row is less than n, then observation j was merged at this stage. If j ≥ n then the merge was with the cluster formed at the (earlier) stage j-n of the algorithm.

    height

    a set of n-1 non-decreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.

  15. def dendrogram(hc: HierarchicalClustering): Window

    A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.

    A dendrogram is a tree diagram to illustrate the arrangement of the clusters produced by hierarchical clustering.

    hc

    hierarchical clustering object.

  16. def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  17. def ensuring(cond: (Operators) ⇒ Boolean): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  18. def ensuring(cond: Boolean, msg: ⇒ Any): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  19. def ensuring(cond: Boolean): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def formatted(fmtstr: String): String
    Implicit
    This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  24. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  25. def grid(data: Array[Array[Array[Double]]]): Window

    2D grid plot.

    2D grid plot.

    data

    an m x n x 2 array which are coordinates of m x n grid.

  26. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  27. def heatmap(rowLabels: Array[String], columnLabels: Array[String], z: Array[Array[Double]], palette: Array[Color]): Window

    Pseudo heat map plot.

    Pseudo heat map plot.

    rowLabels

    the labels for rows of data matrix.

    columnLabels

    the labels for columns of data matrix.

    z

    a data matrix to be shown in pseudo heat map.

    palette

    the color palette.

  28. def heatmap(rowLabels: Array[String], columnLabels: Array[String], z: Array[Array[Double]]): Window

    Pseudo heat map plot.

    Pseudo heat map plot.

    rowLabels

    the labels for rows of data matrix.

    columnLabels

    the labels for columns of data matrix.

    z

    a data matrix to be shown in pseudo heat map.

  29. def heatmap(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Window

    Pseudo heat map plot.

    Pseudo heat map plot.

    x

    x coordinate of data matrix cells. Must be in ascending order.

    y

    y coordinate of data matrix cells. Must be in ascending order.

    z

    a data matrix to be shown in pseudo heat map.

    palette

    the color palette.

  30. def heatmap(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Window

    Pseudo heat map plot.

    Pseudo heat map plot.

    x

    x coordinate of data matrix cells. Must be in ascending order.

    y

    y coordinate of data matrix cells. Must be in ascending order.

    z

    a data matrix to be shown in pseudo heat map.

  31. def heatmap(z: Array[Array[Double]], palette: Array[Color]): Window

    Pseudo heat map plot.

    Pseudo heat map plot.

    z

    a data matrix to be shown in pseudo heat map.

    palette

    the color palette.

  32. def heatmap(z: Array[Array[Double]]): Window

    Pseudo heat map plot.

    Pseudo heat map plot.

    z

    a data matrix to be shown in pseudo heat map.

  33. def hexmap(labels: Array[Array[String]], z: Array[Array[Double]], palette: Array[Color]): Window

    Heat map with hex shape.

    Heat map with hex shape.

    labels

    the descriptions of each cell in the data matrix.

    z

    a data matrix to be shown in pseudo heat map.

    palette

    the color palette.

  34. def hexmap(labels: Array[Array[String]], z: Array[Array[Double]]): Window

    Heat map with hex shape.

    Heat map with hex shape.

    labels

    the descriptions of each cell in the data matrix.

    z

    a data matrix to be shown in pseudo heat map.

  35. def hexmap(z: Array[Array[Double]], palette: Array[Color]): Window

    Heat map with hex shape.

    Heat map with hex shape.

    z

    a data matrix to be shown in pseudo heat map.

    palette

    the color palette.

  36. def hexmap(z: Array[Array[Double]]): Window

    Heat map with hex shape.

    Heat map with hex shape.

    z

    a data matrix to be shown in pseudo heat map.

  37. def hist(data: Array[Array[Double]], xbins: Int, ybins: Int): Window

    3D histogram plot.

    3D histogram plot.

    data

    a sample set.

    xbins

    the number of bins on x-axis.

    ybins

    the number of bins on y-axis.

  38. def hist(data: Array[Array[Double]], k: Int): Window

    3D histogram plot.

    3D histogram plot.

    data

    a sample set.

    k

    the number of bins.

  39. def hist(data: Array[Array[Double]]): Window

    3D histogram plot.

    3D histogram plot.

    data

    a sample set.

  40. def hist(data: Array[Double], breaks: Array[Double]): Window

    Histogram plot.

    Histogram plot.

    data

    a sample set.

    breaks

    an array of size k+1 giving the breakpoints between histogram cells. Must be in ascending order.

  41. def hist(data: Array[Double], k: Int): Window

    Histogram plot.

    Histogram plot.

    data

    a sample set.

    k

    the number of bins.

  42. def hist(data: Array[Double]): Window

    Histogram plot.

    Histogram plot.

    data

    a sample set.

  43. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  44. def line(data: Array[Array[Double]], style: Style = Line.Style.SOLID, color: Color = Color.BLACK, legend: Char = ' '): Window

    Line plot.

    Line plot.

    data

    a n-by-2 or n-by-3 matrix that describes coordinates of points.

    style

    the stroke style of line.

    color

    the color of line.

    legend

    the legend used to draw data points. The default value ' ' makes the point indistinguishable from the line on purpose.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  45. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  46. final def notify(): Unit
    Definition Classes
    AnyRef
  47. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  48. def plot(x: Array[Array[Double]], y: Array[Double], model: Regression[Array[Double]]): Window

    Plots the regression surface.

    Plots the regression surface.

    x

    training data.

    y

    response variable.

    model

    regression model.

  49. def plot(x: Array[Array[Double]], y: Array[Int], model: Classifier[Array[Double]]): Window

    Plots the classification boundary.

    Plots the classification boundary.

    x

    training data.

    y

    training label.

    model

    classification model.

  50. def plot(data: AttributeDataset, legend: Array[Char], palette: Array[Color]): JFrame

    Plot a grid of scatter plots of for all attribute pairs in the attribute data of which the response variable is integer.

    Plot a grid of scatter plots of for all attribute pairs in the attribute data of which the response variable is integer.

    data

    an attribute frame.

    legend

    the legend for each class.

    palette

    the color for each class.

    returns

    the window frame.

  51. def plot(data: AttributeDataset, legend: Char, palette: Array[Color]): JFrame

    Plot a grid of scatter plots of for all attribute pairs in the attribute data of which the response variable is integer.

    Plot a grid of scatter plots of for all attribute pairs in the attribute data of which the response variable is integer.

    data

    an attribute frame.

    legend

    the legend for all classes.

    palette

    the color for each class.

    returns

    the window frame.

  52. def plot(data: AttributeDataset, legend: Char): JFrame

    Plot a grid of scatter plots of for all attribute pairs in the attribute data.

    Plot a grid of scatter plots of for all attribute pairs in the attribute data.

    data

    an attribute frame.

    legend

    the legend for all classes.

    returns

    the window frame.

  53. def plot(data: Array[Array[Double]], label: Array[Int], legend: Array[Char], palette: Array[Color]): Window

    Scatter plot.

    Scatter plot.

    data

    a n-by-2 or n-by-3 matrix that describes coordinates of points.

    label

    the class labels of data.

    legend

    the legend for each class.

    palette

    the color for each class.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  54. def plot(data: Array[Array[Double]], label: Array[Int], legend: Char, palette: Array[Color]): Window

    Scatter plot.

    Scatter plot.

    data

    a n-by-2 or n-by-3 matrix that describes coordinates of points.

    label

    the class labels of data.

    legend

    the legend for all classes.

    palette

    the color for each class.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  55. def plot(data: Array[Array[Double]], labels: Array[String]): Window

    Scatter plot.

    Scatter plot.

    data

    a n-by-2 or n-by-3 matrix that describes coordinates of points.

    labels

    labels of points.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  56. def plot(data: Array[Array[Double]], legend: Char = '*', color: Color = Color.BLACK): Window

    Scatter plot.

    Scatter plot.

    data

    a n-by-2 or n-by-3 matrix that describes coordinates of points.

    legend

    the legend used to draw points.

    • . : dot
    • + : +
    • - : -
    • | : |
    • * : star
    • x : x
    • o : circle
    • O : large circle
    • @ : solid circle
    • # : large solid circle
    • s : square
    • S : large square
    • q : solid square
    • Q : large solid square
    • others : dot
    color

    the color used to draw points.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  57. def qqplot(x: Array[Int], y: Array[Int]): Window

    QQ plot of two sample sets.

    QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.

    x

    a sample set.

    y

    a sample set.

  58. def qqplot(x: Array[Int], d: DiscreteDistribution): Window

    QQ plot of samples to given distribution.

    QQ plot of samples to given distribution. The x-axis is the quantiles of x and the y-axis is the quantiles of given distribution.

    x

    a sample set.

    d

    a distribution.

  59. def qqplot(x: Array[Double], y: Array[Double]): Window

    QQ plot of two sample sets.

    QQ plot of two sample sets. The x-axis is the quantiles of x and the y-axis is the quantiles of y.

    x

    a sample set.

    y

    a sample set.

  60. def qqplot(x: Array[Double], d: Distribution): Window

    QQ plot of samples to given distribution.

    QQ plot of samples to given distribution. The x-axis is the quantiles of x and the y-axis is the quantiles of given distribution.

    x

    a sample set.

    d

    a distribution.

  61. def qqplot(x: Array[Double]): Window

    QQ plot of samples to standard normal distribution.

    QQ plot of samples to standard normal distribution. The x-axis is the quantiles of x and the y-axis is the quantiles of normal distribution.

    x

    a sample set.

  62. def screeplot(pca: PCA): Window

    The scree plot is a useful visual aid for determining an appropriate number of principal components.

    The scree plot is a useful visual aid for determining an appropriate number of principal components. The scree plot graphs the eigenvalue against the component number. To determine the appropriate number of components, we look for an "elbow" in the scree plot. The component number is taken to be the point at which the remaining eigenvalues are relatively small and all about the same size.

    pca

    principal component analysis object.

  63. def spy(matrix: SparseMatrix): Window

    Visualize sparsity pattern.

    Visualize sparsity pattern.

    matrix

    a sparse matrix.

  64. def staircase(data: Array[Double]*): Window

    Create a plot canvas with the staircase line plot.

    Create a plot canvas with the staircase line plot.

    data

    a n x 2 or n x 3 matrix that describes coordinates of points.

  65. def surface(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Window

    3D surface plot.

    3D surface plot.

    x

    the x-axis values of surface.

    y

    the y-axis values of surface.

    z

    the z-axis values of surface.

    palette

    the color palette.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  66. def surface(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Window

    3D surface plot.

    3D surface plot.

    x

    the x-axis values of surface.

    y

    the y-axis values of surface.

    z

    the z-axis values of surface.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  67. def surface(z: Array[Array[Double]], palette: Array[Color]): Window

    3D surface plot.

    3D surface plot.

    z

    the z-axis values of surface.

    palette

    the color palette.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  68. def surface(z: Array[Array[Double]]): Window

    3D surface plot.

    3D surface plot.

    z

    the z-axis values of surface.

    returns

    a tuple of window frame and plot canvas which can be added other shapes.

  69. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  70. def toString(): String
    Definition Classes
    AnyRef → Any
  71. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  72. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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  73. final def wait(arg0: Long): Unit
    Definition Classes
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    @throws( ... )
  74. def wireframe(vertices: Array[Array[Double]], edges: Array[Array[Int]]): Window

    Wire frame plot.

    Wire frame plot. A wire frame model specifies each edge of the physical object where two mathematically continuous smooth surfaces meet, or by connecting an object's constituent vertices using straight lines or curves.

    vertices

    a n-by-2 or n-by-3 array which are coordinates of n vertices.

    edges

    an m-by-2 array of which each row is the vertex indices of two end points of each edge.

  75. def [B](y: B): (Operators, B)
    Implicit
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
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Inherited from Any

Inherited by implicit conversion any2stringadd from Operators to any2stringadd[Operators]

Inherited by implicit conversion StringFormat from Operators to StringFormat[Operators]

Inherited by implicit conversion Ensuring from Operators to Ensuring[Operators]

Inherited by implicit conversion ArrowAssoc from Operators to ArrowAssoc[Operators]

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