package swing
Swing based data visualization.
- Alphabetic
- By Inheritance
- swing
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Type Members
Value Members
- def boxplot(data: Array[Array[Double]], labels: Array[String]): Canvas
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
the plot canvas which can be added other shapes.
- def boxplot(data: Array[Double]*): Canvas
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
the plot canvas which can be added other shapes.
- def canvas2Image(canvas: Canvas, width: Int = 600, height: Int = 600): String
Returns the HTML img tag with the canvas is encoded by BASE64.
- def component2Image(canvas: JComponent, width: Int = 600, height: Int = 600): String
Returns the HTML img tag with the canvas is encoded by BASE64.
- def contour(x: Array[Double], y: Array[Double], z: Array[Array[Double]]): Canvas
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
the plot canvas which can be added other shapes.
- def contour(z: Array[Array[Double]], levels: Array[Double]): Canvas
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.
- returns
the plot canvas which can be added other shapes.
- def contour(z: Array[Array[Double]]): Canvas
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
the plot canvas which can be added other shapes.
- def dendrogram(merge: Array[Array[Int]], height: Array[Double]): Canvas
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): Canvas
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 grid(data: Array[Array[Array[Double]]]): Canvas
2D grid plot.
2D grid plot.
- data
an m x n x 2 array which are coordinates of m x n grid.
- def heatmap(rowLabels: Array[String], columnLabels: Array[String], z: Array[Array[Double]], palette: Array[Color]): Canvas
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(x: Array[Double], y: Array[Double], z: Array[Array[Double]], palette: Array[Color]): Canvas
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(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas
Pseudo heat map plot.
Pseudo heat map plot.
- z
a data matrix to be shown in pseudo heat map.
- palette
the color palette.
- def hexmap(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas
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 hist(data: Array[Double], breaks: Array[Double], prob: Boolean, color: Color): Canvas
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 = 10, prob: Boolean = false, color: Color = Color.BLUE): Canvas
Histogram plot.
Histogram plot.
- data
a sample set.
- k
the number of bins.
- def hist3(data: Array[Array[Double]], xbins: Int = 10, ybins: Int = 10, prob: Boolean = false, palette: Array[Color] = Palette.jet(16)): Canvas
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 line(data: Array[Array[Double]], style: Style = Line.Style.SOLID, color: Color = Color.BLACK, mark: Char = ' ', label: String = null): Canvas
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.
- mark
the mark used to draw data points. The default value ' ' makes the point indistinguishable from the line on purpose.
- returns
the plot canvas which can be added other shapes.
- def plot(data: DataFrame, category: String, mark: Char): PlotGroup
Plot a grid of scatter plots of for all attribute pairs in the data frame of which the response variable is integer.
Plot a grid of scatter plots of for all attribute pairs in the data frame of which the response variable is integer.
- data
an attribute frame.
- mark
the legend for all classes.
- returns
the plot panel.
- def plot(data: DataFrame, mark: Char, color: Color): PlotGroup
Plot a grid of scatter plots of for all attribute pairs in the data frame.
Plot a grid of scatter plots of for all attribute pairs in the data frame.
- data
a data frame.
- mark
the legend for all classes.
- returns
the plot panel.
- def plot(data: DataFrame, x: String, y: String, z: String, category: String, mark: Char): Canvas
Scatter plot.
Scatter plot.
- data
the data frame.
- x
the column as x-axis.
- y
the column as y-axis.
- z
the column as z-axis.
- category
the category column for coloring.
- returns
the plot canvas which can be added other shapes.
- def plot(data: DataFrame, x: String, y: String, z: String, mark: Char, color: Color): Canvas
Scatter plot.
Scatter plot.
- data
the data frame.
- x
the column as x-axis.
- y
the column as y-axis.
- z
the column as z-axis.
- returns
the plot canvas which can be added other shapes.
- def plot(data: DataFrame, x: String, y: String, category: String, mark: Char): Canvas
Scatter plot.
Scatter plot.
- data
the data frame.
- x
the column as x-axis.
- y
the column as y-axis.
- category
the category column for coloring.
- returns
the plot canvas which can be added other shapes.
- def plot(data: DataFrame, x: String, y: String, mark: Char, color: Color): Canvas
Scatter plot.
Scatter plot.
- data
the data frame.
- x
the column as x-axis.
- y
the column as y-axis.
- returns
the plot canvas which can be added other shapes.
- def plot(x: Array[Array[Double]], y: Array[Int], mark: Char): Canvas
Scatter plot.
Scatter plot.
- x
a n-by-2 or n-by-3 matrix that describes coordinates of points.
- y
class label.
- returns
the plot canvas which can be added other shapes.
- def plot(x: Array[Array[Double]], y: Array[String], mark: Char): Canvas
Scatter plot.
Scatter plot.
- x
a n-by-2 or n-by-3 matrix that describes coordinates of points.
- y
labels of points.
- returns
the plot canvas which can be added other shapes.
- def plot(x: Array[Array[Double]], mark: Char = '*', color: Color = Color.BLACK): Canvas
Scatter plot.
Scatter plot.
- x
a n-by-2 or n-by-3 matrix that describes coordinates of points.
- mark
the mark 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
the plot canvas which can be added other shapes.
- def qqplot(x: Array[Int], y: Array[Int]): Canvas
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): Canvas
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]): Canvas
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): Canvas
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]): Canvas
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): Canvas
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, k: Int = 1): Canvas
Visualize sparsity pattern.
Visualize sparsity pattern.
- matrix
a sparse matrix.
- def staircase(data: Array[Array[Double]], color: Color = Color.BLACK, label: String = null): Canvas
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]): Canvas
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
the plot canvas which can be added other shapes.
- def surface(z: Array[Array[Double]], palette: Array[Color] = Palette.jet(16)): Canvas
3D surface plot.
3D surface plot.
- z
the z-axis values of surface.
- palette
the color palette.
- returns
the plot canvas which can be added other shapes.
- def text(texts: Array[String], coordinates: Array[Array[Double]]): Canvas
Text plot.
Text plot.
- texts
the texts.
- coordinates
a n-by-2 or n-by-3 matrix that are the coordinates of texts.
- def wireframe(vertices: Array[Array[Double]], edges: Array[Array[Int]]): Canvas
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.
- object Window extends Serializable
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.