dyson_equalizer.examples module#
- dyson_equalizer.examples.generate_X(m: int = 1000, n: int = 2000, seed: int = 123) ndarray[source]#
Generates a signal matrix with 10 strong principal values and 10 weak principal values.
- Parameters:
- m: int, optional
The number of rows (default=1000)
- n: int, optional
The number of rows (default=2000)
- seed: int, optional
The random seed (default=123)
- Returns:
- numpy.ndarray
The data matrix
- dyson_equalizer.examples.generate_Y_with_almost_homoskedastic_noise(m: int = 1000, n: int = 2000, seed: int = 123) ndarray[source]#
Generates a test matrix with 10 strong principal values and 10 weak principal values.
The noise is homoskedastic except for the last 5 rows and columns where it is abnormally strong
- Parameters:
- m: int, optional
The number of rows (default=1000)
- n: int, optional
The number of rows (default=2000)
- seed: int, optional
The random seed (default=123)
- Returns:
- numpy.ndarray
The data matrix
See also
- dyson_equalizer.examples.generate_Y_with_heteroskedastic_noise(m: int = 1000, n: int = 2000, noise_dimensions: int = 10, seed: int = 123) ndarray[source]#
Generates a test matrix with 10 strong principal values and 10 weak principal values.
- Parameters:
- m: int, optional
The number of rows (default=1000)
- n: int, optional
The number of rows (default=2000)
- noise_dimensions: int, optional
The number of noise dimensions (default=10)
- seed: int, optional
The random seed (default=123)
- Returns:
- numpy.ndarray
The data matrix
See also