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A Flatline expression is a lisp-like expresion that allows you to make references and process columns and rows of the origin dataset. See the full Flatline reference here. Let's see a first example that clones a dataset and adds a new field named "Celsius" to it using an expression that converts the values from the "Fahrenheit" field to Celsius.
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A new field can actually generate multiple fields. In that case their names can be specified using the names arguments.
In addition to horizontally selecting different fields in the same row, you can keep the field fixed bitcoin ausner select vertical windows of its value, via the window and related operators.
For example, the following request will generate a new field using a sliding window of 7 values for the field named "Fahrenheit" and will also generate two additional fields named "Yesterday" and "Tomorrow" with the previous and next value of the current row for the field 0.
The list of values generated from each input row that way constitutes an output row of the generated dataset.
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Ką dar derėtų žinoti Android TV box priedėlio naudotojui? See the table below for more details. See the Section on filtering rows for more details.
Example: "description": "This field is a bitcoin ausner descriptions optional Array A description for every of the new fields generated. Example: "fields": " window Price -2 0 " label optional Array Label of the new field. Example: "label": "New price" labels Array Labels for each of the new bitcoin ausner generated. Example: "name": "Price" names optional Array Names for each of the new fields generated. Basically, a Flatline expresion can easily be translated to its json-like variant and vice versa by just changing parentheses to brackets, symbols to quoted strings, and adding commas to separate each sub-expression.
For example, the following two expressions are the same for BigML.
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If you specify both sampling and filtering arguments, the former are applied first. As with filters applied to datasources, dataset filters can use the full Flatline language to specify the boolean expression to use when sifting the input.
Flatline performs type inference, and will in general figure out the proper optype for the generated fields, which are subsequently summarized by the dataset creation process, reaching then their final datatype just as with a regular dataset created from a datasource.