Creating a Leap Frame

Let's create a leap frame to hold our data.

import ml.combust.mleap.runtime._
import ml.combust.mleap.core.types._

// Create a schema. Returned as a Try monad to ensure that there
// Are no duplicate field names
val schema: StructType = StructType(StructField("a_string", ScalarType.String),
  StructField("a_double", ScalarType.Double),
  StructField("a_float", ScalarType.Float),
  StructField("an_int", ScalarType.Int),
  StructField("a_long", ScalarType.Long)).get

// Create a dataset to contain all of our values
// This dataset has two rows
val dataset: LocalDataset = LocalDataset(Row("Hello, MLeap!", 56.7d, 13.0f, 42, 67l),
  Row("Another row", 23.4d, 11.0f, 43, 88l))

// Create a LeapFrame from the schema and dataset
val leapFrame: DefaultLeapFrame = LeapFrame(schema, dataset)

// Make some assertions about the data in our leap frame
assert(leapFrame.dataset(0).getString(0) == "Hello, MLeap!")
assert(leapFrame.dataset(0).getDouble(1) == 56.7d)
assert(leapFrame.dataset(1).getDouble(1) == 23.4d)

Programatically creating leap frames like this can be very useful for scoring data from, say, a web server or some other user input. It is also useful to be able to load data from files and store data for later use. See our section on serializing leap frames for more information.

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