trait Operators extends AnyRef
Data visualization operators.
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def
##(): Int
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==(arg0: Any): Boolean
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asInstanceOf[T0]: T0
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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.

def
boxplot(data: Array[Double]*): Window
A box plot is a convenient way of graphically depicting groups of numerical data through their fivenumber 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 fivenumber 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 nonparametric. 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 q_{1}, the median q_{2} and third quartile q_{3}.  Calculate the interquartile range (IQR) by subtracting the first quartile from the third quartile. (q_{3} ? q_{1})
 Construct a box above the number line bounded on the bottom by the first quartile (q_{1}) and on the top by the third quartile (q_{3}).
 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.

def
clone(): AnyRef
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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 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D 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.

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 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D 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.

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 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D 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.

def
contour(z: Array[Array[Double]]): Window
Contour plot.
Contour plot. A contour plot is a graphical technique for representing a 3dimensional surface by plotting constant z slices, called contours, on a 2dimensional 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 3D 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.

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 n1 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 jn of the algorithm.
 height
a set of n1 nondecreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.

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.
 def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
 def ensuring(cond: (Operators) ⇒ Boolean): Operators
 def ensuring(cond: Boolean, msg: ⇒ Any): Operators
 def ensuring(cond: Boolean): Operators

final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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 def formatted(fmtstr: String): String

final
def
getClass(): Class[_]
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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.

def
hashCode(): Int
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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 xaxis.
 ybins
the number of bins on yaxis.

def
hist(data: Array[Array[Double]], k: Int): Window
3D histogram plot.
3D histogram plot.
 data
a sample set.
 k
the number of bins.

def
hist(data: Array[Array[Double]]): Window
3D histogram plot.
3D histogram plot.
 data
a sample set.

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.

def
hist(data: Array[Double], k: Int): Window
Histogram plot.
Histogram plot.
 data
a sample set.
 k
the number of bins.

def
hist(data: Array[Double]): Window
Histogram plot.
Histogram plot.
 data
a sample set.

final
def
isInstanceOf[T0]: Boolean
 Definition Classes
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def
line(data: Array[Array[Double]], style: Style = Line.Style.SOLID, color: Color = Color.BLACK, legend: Char = ' '): Window
Line plot.
Line plot.
 data
a nby2 or nby3 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.

final
def
ne(arg0: AnyRef): Boolean
 Definition Classes
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final
def
notify(): Unit
 Definition Classes
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final
def
notifyAll(): Unit
 Definition Classes
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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.

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.

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.

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.

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.

def
plot(data: Array[Array[Double]], label: Array[Int], legend: Array[Char], palette: Array[Color]): Window
Scatter plot.
Scatter plot.
 data
a nby2 or nby3 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.

def
plot(data: Array[Array[Double]], label: Array[Int], legend: Char, palette: Array[Color]): Window
Scatter plot.
Scatter plot.
 data
a nby2 or nby3 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.

def
plot(data: Array[Array[Double]], labels: Array[String]): Window
Scatter plot.
Scatter plot.
 data
a nby2 or nby3 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.

def
plot(data: Array[Array[Double]], legend: Char = '*', color: Color = Color.BLACK): Window
Scatter plot.
Scatter plot.
 data
a nby2 or nby3 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.

def
qqplot(x: Array[Int], y: Array[Int]): Window
QQ plot of two sample sets.
QQ plot of two sample sets. The xaxis is the quantiles of x and the yaxis is the quantiles of y.
 x
a sample set.
 y
a sample set.

def
qqplot(x: Array[Int], d: DiscreteDistribution): Window
QQ plot of samples to given distribution.
QQ plot of samples to given distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of given distribution.
 x
a sample set.
 d
a distribution.

def
qqplot(x: Array[Double], y: Array[Double]): Window
QQ plot of two sample sets.
QQ plot of two sample sets. The xaxis is the quantiles of x and the yaxis is the quantiles of y.
 x
a sample set.
 y
a sample set.

def
qqplot(x: Array[Double], d: Distribution): Window
QQ plot of samples to given distribution.
QQ plot of samples to given distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of given distribution.
 x
a sample set.
 d
a distribution.

def
qqplot(x: Array[Double]): Window
QQ plot of samples to standard normal distribution.
QQ plot of samples to standard normal distribution. The xaxis is the quantiles of x and the yaxis is the quantiles of normal distribution.
 x
a sample set.

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.

def
spy(matrix: SparseMatrix): Window
Visualize sparsity pattern.
Visualize sparsity pattern.
 matrix
a sparse matrix.

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.

def
surface(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Window
3D surface plot.
3D surface plot.
 x
the xaxis values of surface.
 y
the yaxis values of surface.
 z
the zaxis values of surface.
 palette
the color palette.
 returns
a tuple of window frame and plot canvas which can be added other shapes.

def
surface(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Window
3D surface plot.
3D surface plot.
 x
the xaxis values of surface.
 y
the yaxis values of surface.
 z
the zaxis values of surface.
 returns
a tuple of window frame and plot canvas which can be added other shapes.

def
surface(z: Array[Array[Double]], palette: Array[Color]): Window
3D surface plot.
3D surface plot.
 z
the zaxis values of surface.
 palette
the color palette.
 returns
a tuple of window frame and plot canvas which can be added other shapes.

def
surface(z: Array[Array[Double]]): Window
3D surface plot.
3D surface plot.
 z
the zaxis values of surface.
 returns
a tuple of window frame and plot canvas which can be added other shapes.

final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
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wait(arg0: Long): Unit
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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 nby2 or nby3 array which are coordinates of n vertices.
 edges
an mby2 array of which each row is the vertex indices of two end points of each edge.
 def →[B](y: B): (Operators, B)
High level Smile operators in Scala.