Compute the iSAX index for a DataFrame which is assumed to be numeric time series data
h2o.isax(x, num_words, max_cardinality, optimize_card = FALSE)
x | an H2OFrame |
---|---|
num_words | Number of iSAX words for the timeseries. ie granularity along the time series |
max_cardinality | Maximum cardinality of the iSAX word. Each word can have less than the max |
optimize_card | An optimization flag that will find the max cardinality regardless of what is passed in for max_cardinality. |
An H2OFrame with the name of time series, string representation of iSAX word, followed by binary representation
https://www.cs.ucr.edu/~eamonn/iSAX_2.0.pdf
https://www.cs.ucr.edu/~eamonn/SAX.pdf
# NOT RUN { library(h2o) h2o.init() df <- h2o.createFrame(rows = 1, cols = 256, randomize = TRUE, value = 0, real_range = 100, categorical_fraction = 0, factors = 0, integer_fraction = 0, integer_range = 100, binary_fraction = 0, binary_ones_fraction = 0, time_fraction = 0, string_fraction = 0, missing_fraction = 0, has_response = FALSE, seed = 123) df2 <- h2o.cumsum(df, axis = 1) h2o.isax(df2, num_words = 10, max_cardinality = 10) # }