dcase_util.processors.NormalizationProcessor

class dcase_util.processors.NormalizationProcessor(n=None, s1=None, s2=None, mean=None, std=None, normalizer=None, filename=None, **kwargs)[source]

Data normalizer to accumulate data statistics

__init__ method.

Parameters
nint

Item count used to calculate statistics Default value None

s1numpy.array [shape=(vector_length,)]

Vector-wise sum of the data seen by the Normalizer Default value None

s2numpy.array [shape=(vector_length,)]

Vector-wise sum^2 of the data seen by the Normalizer Default value None

meannumpy.ndarray() [shape=(vector_length, 1)]

Mean of the data Default value None

stdnumpy.ndarray() [shape=(vector_length, 1)]

Standard deviation of the data Default value None

normalizerNormalizer

Normalizer object to initialize the processor Default value None

filenamestr

Filename to saved normalizer object to initialize the processor Default value None

__init__(n=None, s1=None, s2=None, mean=None, std=None, normalizer=None, filename=None, **kwargs)[source]

__init__ method.

Parameters
nint

Item count used to calculate statistics Default value None

s1numpy.array [shape=(vector_length,)]

Vector-wise sum of the data seen by the Normalizer Default value None

s2numpy.array [shape=(vector_length,)]

Vector-wise sum^2 of the data seen by the Normalizer Default value None

meannumpy.ndarray() [shape=(vector_length, 1)]

Mean of the data Default value None

stdnumpy.ndarray() [shape=(vector_length, 1)]

Standard deviation of the data Default value None

normalizerNormalizer

Normalizer object to initialize the processor Default value None

filenamestr

Filename to saved normalizer object to initialize the processor Default value None

Methods

__init__([n, s1, s2, mean, std, normalizer, ...])

__init__ method.

delimiter([exclude_delimiters])

Use csv.sniffer to guess delimiter for CSV file

detect_file_format([filename])

Detect file format from extension

empty()

Check if file is empty

exists()

Checks that file exists

get_file_information()

Get file information, filename

get_processing_chain_item()

Get processing chain item with current Processor data.

is_package([filename])

Determine if the file is compressed package.

load([filename])

Load file

log([level])

Log container content

process([data, store_processing_chain])

Normalize feature matrix with internal statistics of the class

save([filename])

Save file

show([mode, indent, visualize])

Print container content

to_html([indent])

Get container information in a HTML formatted string

to_string([ui, indent])

Get container information in a string

validate_format()

Validate file format

Attributes

bytes

File size in bytes

input_type

Input data type

logger

Logger instance

md5

Checksum for file.

output_type

Output data type

valid_formats

Valid file formats