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- def field(field: JsValue, type: String, bin: Either[Boolean, JsObject] = Left(false), timeUnit: String = "", aggregate: String = "", title: String = ""): JsObject
Returns a field definition.
Returns a field definition. To encode a particular field in the data set with an encoding channel, the channel’s field definition must describe the field name and its data type.
- field
A string defining the name of the field from which to pull a data value or an object defining iterated values from the repeat operator. field is not required if aggregate is count.
- bin
A flag for binning a quantitative field.
- If true, default binning parameters will be applied.
- If "binned", this indicates that the data for the x (or y) channel are already binned.
- timeUnit
Time unit (e.g., year, yearmonth, month, hours) for a temporal field, or a temporal field that gets casted as ordinal.
- aggregate
Aggregation function for the field (e.g., "mean", "sum", "median", "min", "max", "count").
- title
A title for the field. If null, the title will be removed. The default value is derived from the field’s name and transformation function (aggregate, bin and timeUnit).
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- def markPropField(field: JsValue, type: String, bin: Either[Boolean, JsObject] = Left(false), timeUnit: String = "", aggregate: String = "", scale: JsValue = JsUndefined, legend: JsValue = JsUndefined, condition: JsValue = JsUndefined): JsObject
Returns a mark property field Definition.
Returns a mark property field Definition. x and y position channels determine the position of the marks, or width/height of horizontal/vertical "area" and "bar". In addition, x2 and y2 can specify the span of ranged area, bar, rect, and rule.
- field
A string defining the name of the field from which to pull a data value or an object defining iterated values from the repeat operator. field is not required if aggregate is count
- bin
A flag for binning a quantitative field.
- If true, default binning parameters will be applied.
- If "binned", this indicates that the data for the x (or y) channel are already binned.
- timeUnit
Time unit (e.g., year, yearmonth, month, hours) for a temporal field, or a temporal field that gets casted as ordinal.
- aggregate
Aggregation function for the field (e.g., "mean", "sum", "median", "min", "max", "count").
- scale
An object defining properties of the channel’s scale, which is the function that transforms values in the data domain (numbers, dates, strings, etc) to visual values (pixels, colors, sizes) of the encoding channels. If null, the scale will be disabled and the data value will be directly encoded. If undefined, default scale properties are applied.
- legend
An object defining properties of the legend. If null, the legend for the encoding channel will be removed.
- condition
One or more value definition(s) with a selection or a test predicate. Note: A field definition’s condition property can only contain conditional value definitions since Vega-Lite only allows at most one encoded field per encoding channel.
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- def polarField(field: JsValue, type: String, bin: Either[Boolean, JsObject] = Left(false), timeUnit: String = "", aggregate: String = "", title: String = "", scale: JsValue = JsUndefined, sort: Option[String] = None, stack: JsValue = JsUndefined): JsObject
Returns a polar field definition.
Returns a polar field definition. theta and radius position channels determine the position or interval on polar coordindates for arc and text marks.
- field
A string defining the name of the field from which to pull a data value or an object defining iterated values from the repeat operator. field is not required if aggregate is count
- bin
A flag for binning a quantitative field.
- If true, default binning parameters will be applied.
- If "binned", this indicates that the data for the x (or y) channel are already binned.
- timeUnit
Time unit (e.g., year, yearmonth, month, hours) for a temporal field, or a temporal field that gets casted as ordinal.
- aggregate
Aggregation function for the field (e.g., "mean", "sum", "median", "min", "max", "count").
- title
A title for the field. If null, the title will be removed. The default value is derived from the field’s name and transformation function (aggregate, bin and timeUnit).
- scale
An object defining properties of the channel’s scale, which is the function that transforms values in the data domain (numbers, dates, strings, etc) to visual values (pixels, colors, sizes) of the encoding channels. If null, the scale will be disabled and the data value will be directly encoded. If undefined, default scale properties are applied.
- sort
Sort order for the encoded field. For continuous fields (quantitative or temporal), sort can be either "ascending" or "descending". For discrete fields, sort can be one of the following:
- "ascending" or "descending" – for sorting by the values’ natural order in JavaScript.
- A string indicating an encoding channel name to sort by (e.g., "x" or "y") with an optional minus prefix for descending sort (e.g., "-x" to sort by x-field, descending). This channel string is short-form of a sort-by-encoding definition. For example, "sort": "-x" is equivalent to "sort": {"encoding": "x", "order": "descending"}.
- stack
Type of stacking offset if the field should be stacked. stack is only applicable for x, y, theta, and radius channels with continuous domains. For example, stack of y can be used to customize stacking for a vertical bar chart. stack can be one of the following values:
- "zero" or true: stacking with baseline offset at zero value of the scale (for creating typical stacked bar and area chart).
- "normalize": stacking with normalized domain (for creating normalized stacked bar and area charts.
- "center": stacking with center baseline (for streamgraph).
- null or false: No-stacking. This will produce layered bar and area chart.
- def positionField(field: JsValue, type: String, bin: Either[Boolean, JsObject] = Left(false), timeUnit: String = "", aggregate: String = "", title: String = "", scale: JsValue = JsUndefined, axis: JsValue = JsUndefined, sort: Option[String] = None, band: Option[Double] = None, impute: JsValue = JsUndefined, stack: JsValue = JsUndefined): JsObject
Returns a position field definition.
Returns a position field definition. x and y position channels determine the position of the marks, or width/height of horizontal/vertical "area" and "bar". In addition, x2 and y2 can specify the span of ranged area, bar, rect, and rule.
- field
A string defining the name of the field from which to pull a data value or an object defining iterated values from the repeat operator. field is not required if aggregate is count
- bin
A flag for binning a quantitative field.
- If true, default binning parameters will be applied.
- If "binned", this indicates that the data for the x (or y) channel are already binned.
- timeUnit
Time unit (e.g., year, yearmonth, month, hours) for a temporal field, or a temporal field that gets casted as ordinal.
- aggregate
Aggregation function for the field (e.g., "mean", "sum", "median", "min", "max", "count").
- title
A title for the field. If null, the title will be removed. The default value is derived from the field’s name and transformation function (aggregate, bin and timeUnit).
- scale
An object defining properties of the channel’s scale, which is the function that transforms values in the data domain (numbers, dates, strings, etc) to visual values (pixels, colors, sizes) of the encoding channels. If null, the scale will be disabled and the data value will be directly encoded. If undefined, default scale properties are applied.
- axis
An object defining properties of axis’s gridlines, ticks and labels. If null, the axis for the encoding channel will be removed. If undefined, default axis properties are applied.
- sort
Sort order for the encoded field. For continuous fields (quantitative or temporal), sort can be either "ascending" or "descending". For discrete fields, sort can be one of the following:
- "ascending" or "descending" – for sorting by the values’ natural order in JavaScript.
- A string indicating an encoding channel name to sort by (e.g., "x" or "y") with an optional minus prefix for descending sort (e.g., "-x" to sort by x-field, descending). This channel string is short-form of a sort-by-encoding definition. For example, "sort": "-x" is equivalent to "sort": {"encoding": "x", "order": "descending"}.
- band
For rect-based marks (rect, bar, and image), mark size relative to bandwidth of band scales or time units. If set to 1, the mark size is set to the bandwidth or the time unit interval. If set to 0.5, the mark size is half of the bandwidth or the time unit interval. For other marks, relative position on a band of a stacked, binned, time unit or band scale. If set to 0, the marks will be positioned at the beginning of the band. If set to 0.5, the marks will be positioned in the middle of the band.
- impute
An object defining the properties of the Impute Operation to be applied. The field value of the other positional channel is taken as key of the Impute Operation. The field of the color channel if specified is used as groupby of the Impute Operation.
- stack
Type of stacking offset if the field should be stacked. stack is only applicable for x, y, theta, and radius channels with continuous domains. For example, stack of y can be used to customize stacking for a vertical bar chart. stack can be one of the following values:
- "zero" or true: stacking with baseline offset at zero value of the scale (for creating typical stacked bar and area chart).
- "normalize": stacking with normalized domain (for creating normalized stacked bar and area charts.
- "center": stacking with center baseline (for streamgraph).
- null or false: No-stacking. This will produce layered bar and area chart.
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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.