Initialize a UMAP object
Embeddings in low-dimensional space in float32 format, which can be converted to any of the following types: DataFrame, Series, DeviceBuffer.
embeddings.asDataFrame(); // returns DataFrame<{number: Series<Numeric>}>
embeddings.asSeries(); // returns Series<Numeric>
embeddings.asDeviceBuffer(); //returns rmm.DeviceBuffer
Fit features into an embedded space
array containing floats or doubles in the format [x1, y1, z1, x2, y2, z2...] for features x, y & z.
// For a sample dataset of colors, with properties r,g and b:
features = [
...Object.values({ r: xx1, g: xx2, b: xx3 }),
...Object.values({ r: xx4, g: xx5, b: xx6 }),
] // [xx1, xx2, xx3, xx4, xx5, xx6]
array containing target values
// For a sample dataset of colors, with properties r,g and b:
target = [color1, color2] // len(target) = nFeatures
number of properties in the input features, if features is of the format [x1, y1, x2, y2...]
When set to True, the method will automatically convert the inputs to float32
FittedUMAP object with updated embeddings
Fit features into an embedded space
Dense or sparse matrix containing floats or doubles. Acceptable dense formats: cuDF DataFrame
cuDF Series containing target values
When set to True, the method will automatically convert the inputs to float32
FittedUMAP object with updated embeddings
Fit features into an embedded space
cuDF Series containing floats or doubles in the format [x1, y1, z1, x2, y2, z2...] for features x, y & z.
cuDF Series containing target values
number of properties in the input features, if features is of the format [x1,y1,x2,y2...]
When set to True, the method will automatically convert the inputs to float32
FittedUMAP object with updated embeddings
Numeric cudf DataType
Embeddings in low-dimensional space in dtype format, which can be converted to any of the following types: DataFrame, Series, DeviceBuffer.
getEmbeddings(new Float64).asDataFrame(); // returns DataFrame<{number: Series<Numeric>}>
getEmbeddings(new Int32).asSeries(); // returns Series<Numeric>
getEmbeddings(new UInt32).asDeviceBuffer(); //returns rmm.DeviceBuffer
Refine features into existing embedded space as base
array containing floats or doubles in the format [x1, y1, z1, x2, y2, z2...] for features x, y & z.
// For a sample dataset of colors, with properties r,g and b:
features = [
...Object.values({ r: xx1, g: xx2, b: xx3 }),
...Object.values({ r: xx4, g: xx5, b: xx6 }),
] // [xx1, xx2, xx3, xx4, xx5, xx6]
array containing target values
// For a sample dataset of colors, with properties r,g and b:
target = [color1, color2] // len(target) = nFeatures
number of properties in the input features, if features is of the format [x1, y1, x2, y2...]
When set to True, the method will automatically convert the inputs to float32
Refine features into existing embedded space as base
Dense or sparse matrix containing floats or doubles. Acceptable dense formats: cuDF DataFrame
cuDF Series containing target values
When set to True, the method will automatically convert the inputs to float32
Refine features into existing embedded space as base
cuDF Series containing floats or doubles in the format [x1, y1, z1, x2, y2, z2...] for features x, y & z.
cuDF Series containing target values
number of properties in the input features, if features is of the format [x1,y1,x2,y2...]
When set to True, the method will automatically convert the inputs to float32
Transform features into the existing embedded space and return that transformed output.
number of properties in the input features, if features is of the format [x1,y1,x2,y2...]
When set to True, the method will automatically convert the inputs to float32
Transformed features into the existing embedded space and return an Embeddings
instance which can be converted to any of the following types: DataFrame, Series, DeviceBuffer.
transformArray(...).asDataFrame(); // returns DataFrame<{number: Series<Numeric>}>
transformArray(...).asSeries(); // returns Series<Numeric>
transformArray(...).asDeviceBuffer(); //returns rmm.DeviceBuffer
Transform features into the existing embedded space and return that transformed output.
When set to True, the method will automatically convert the inputs to float32
Transformed features into the existing embedded space and return an Embeddings
instance which can be converted to any of the following types: DataFrame, Series, DeviceBuffer
transformDataFrame(...).asDataFrame(); // returns DataFrame<{number: Series<Numeric>}>
transformDataFrame(...).asSeries(); // returns Series<Numeric>
transformDataFrame(...).asDeviceBuffer(); //returns rmm.DeviceBuffer
Transform features into the existing embedded space and return that transformed output.
number of properties in the input features, if features is of the format [x1,y1,x2,y2...]
When set to True, the method will automatically convert the inputs to float32
Transformed features into the existing embedded space and return an Embeddings
instancewhich can be converted to any of the following types: DataFrame, Series, DeviceBuffer.
transformSeries(...).asDataFrame(); // returns DataFrame<{number: Series<Numeric>}>
transformSeries(...).asSeries(); // returns Series<Numeric>
transformSeries(...).asDeviceBuffer(); //returns rmm.DeviceBuffer
Initialize a UMAP object