# Source code for sysidentpy.utils.generate_data

```"""Utilities for data generation """
# Authors:
#           Wilson Rocha Lacerda Junior <wilsonrljr@outlook.com>

import numpy as np

[docs]def get_siso_data(n=5000, colored_noise=False, sigma=0.05, train_percentage=90):
"""Perform the Error Reduction Ration algorithm.

Parameters
----------
n : int
The number of samples.
colored_noise : bool
Select white noise or colored noise (autoregressive noise).
sigma : float
The standard deviation of the random distribution to generate
the noise.
train_percentage : int
The percentage of the data to be used as train data.

Returns
-------
x_train, x_valid : array-like
The input data to be used in identification and validation,
respectively.
y_train, y_valid : array-like
The output data to be used in identification and validation,
respectively.

"""
mu = 0  # mean of the distribution
nu = np.random.normal(mu, sigma, n).T
e = np.zeros((n, 1))

lag = 2
if colored_noise is True:
for k in range(lag, len(e)):
e[k] = 0.8 * nu[k - 1] + nu[k]
else:
e = nu

x = np.random.uniform(-1, 1, n).T
y = np.zeros((n, 1))
theta = np.array([[0.2], [0.1], [0.9]])
lag = 2
for k in range(lag, len(x)):
y[k] = (
theta[0] * y[k - 1]
+ theta[1] * y[k - 1] * x[k - 1]
+ theta[2] * x[k - 2]
+ e[k]
)

split_data = int(len(x) * (train_percentage / 100))

x_train = x[0:split_data].reshape(-1, 1)
x_valid = x[split_data::].reshape(-1, 1)

y_train = y[0:split_data].reshape(-1, 1)
y_valid = y[split_data::].reshape(-1, 1)

return x_train, x_valid, y_train, y_valid

[docs]def get_miso_data(n=5000, colored_noise=False, sigma=0.05, train_percentage=90):
"""Perform the Error Reduction Ration algorithm.

Parameters
----------
n : int
The number of samples.
colored_noise : bool
Select white noise or colored noise (autoregressive noise).
sigma : float
The standard deviation of the random distribution to generate
the noise.
train_percentage : int
The percentage of the data to be used as train data.

Returns
-------
x_train, x_valid : array-like
The input data to be used in identification and validation,
respectively.
y_train, y_valid : array-like
The output data to be used in identification and validation,
respectively.

"""
mu = 0  # mean of the distribution
nu = np.random.normal(mu, sigma, n).T
e = np.zeros((n, 1))

lag = 2
if colored_noise is True:
for k in range(lag, len(e)):
e[k] = 0.8 * nu[k - 1] + nu[k]
else:
e = nu

x1 = np.random.uniform(-1, 1, n).T
x2 = np.random.uniform(-1, 1, n).T
y = np.zeros((n, 1))
theta = np.array([[0.4], [0.1], [0.6], [-0.3]])

lag = 2
for k in range(lag, len(e)):
y[k] = (
theta[0] * y[k - 1] ** 2
+ theta[1] * y[k - 1] * x1[k - 1]
+ theta[2] * x2[k - 1]
+ theta[3] * x1[k - 1] * x2[k - 2]
+ e[k]
)

split_data = int(len(x1) * (train_percentage / 100))
x1_train = x1[0:split_data].reshape(-1, 1)
x2_train = x2[0:split_data].reshape(-1, 1)
x1_valid = x1[split_data::].reshape(-1, 1)
x2_valid = x2[split_data::].reshape(-1, 1)

x_train = np.hstack([x1_train, x2_train])
x_valid = np.hstack([x1_valid, x2_valid])

y_train = y[0:split_data].reshape(-1, 1)
y_valid = y[split_data::].reshape(-1, 1)

return x_train, x_valid, y_train, y_valid
```