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# Simple program that creates a DCT instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.feature.dct import DCT
from pyflink.table import StreamTableEnvironment

# create a new StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()

# create a StreamTableEnvironment
t_env = StreamTableEnvironment.create(env)

# generate input data
input_data = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(1.0, 1.0, 1.0, 1.0),),
        (Vectors.dense(1.0, 0.0, -1.0, 0.0),),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [DenseVectorTypeInfo()])))

# create a DCT object and initialize its parameters
dct = DCT()

# use the dct for feature engineering
output = dct.transform(input_data)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    input_value = result[field_names.index(dct.get_input_col())]
    output_value = result[field_names.index(dct.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))