Operators

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final def !=(arg0: Any): Boolean

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AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
def +(other: String): String

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This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
Definition Classes
any2stringadd
def ->[B](y: B): (Operators, B)

Implicit information
This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc
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@inline()
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
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 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.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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.
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.
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.
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.
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.
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

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: (Operators) ⇒ Boolean): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean, msg: ⇒ Any): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def formatted(fmtstr: String): String

Implicit information
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()
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
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

Definition Classes
AnyRef → Any
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 x-axis.
ybins
the number of bins on y-axis.
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
Any
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.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
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.
def →[B](y: B): (Operators, B)

Implicit information
This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc

Inherited from AnyRef

Inherited from Any

Related Doc: package plot

trait Operators extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: String): String

def ->[B](y: B): (Operators, B)

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def boxplot(data: Array[Array[Double]], labels: Array[String]): Window

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

def clone(): AnyRef

def contour(x: Array[Double], y: Array[Double], z: Array[Array[Double]], levels: Array[Double], palette: Array[Color]): Window

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

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

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

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

def dendrogram(hc: HierarchicalClustering): Window

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

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatted(fmtstr: String): String

final def getClass(): Class[_]

def grid(data: Array[Array[Array[Double]]]): Window

def hashCode(): Int

def heatmap(rowLabels: Array[String], columnLabels: Array[String], z: Array[Array[Double]], palette: Array[Color]): Window

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

final def isInstanceOf[T0]: Boolean

def line(data: Array[Array[Double]], style: Style = Line.Style.SOLID, color: Color = Color.BLACK, legend: Char = ' '): Window

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def plot(x: Array[Array[Double]], y: Array[Double], model: Regression[Array[Double]]): Window

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

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

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

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

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

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

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

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

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

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

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

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

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

def screeplot(pca: PCA): Window

def spy(matrix: SparseMatrix): Window

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

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

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

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

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

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def wireframe(vertices: Array[Array[Double]], edges: Array[Array[Int]]): Window

def →[B](y: B): (Operators, B)

Inherited from AnyRef