iaf.fit.models

Implementation of simple models.

iaf.fit.models.exponential_model(x: ndarray, a: float, b: float)

Exponential model y_hat = a * exp(b * x).

Parameters:

• x (np.ndarray) – Independent variable.

• a (float) – Scaling parameter of the exponential a * exp(b * x).

• b (float) – Scaling parameter of independent variable in the exponential a * exp(b * x).

Returns:

y_hat – Predicted y values.

Return type:

np.ndarray

iaf.fit.models.linear_model(x: ndarray, a: float, b: float)

Linear model y_hat = a * x + b.

Parameters:

• x (np.ndarray) – Independent variable.

• a (float) – Slope of the line ax + b.

• b (float) – Intercept of the line ax + b.

Returns:

y_hat – Predicted y values.

Return type:

np.ndarray

iaf.fit.models.logarithmic_model(x: ndarray, a: float, b: float)

Logarithmic model y_hat = a + b * ln(x).

Parameters:

• x (np.ndarray) – Independent variable.

• a (float) – Intercept value of the logarithmic model a + b * ln(x).

• b (float) – Scaling parameter of independent variable in the logarithmic model a + b * ln(x).

Returns:

y_hat – Predicted y values.

Return type:

np.ndarray

iaf.fit.models.square_root_model(x: ndarray, a: float, b: float)

Square root model y_hat = a + b * sqrt(x).

Parameters:

• x (np.ndarray) – Independent variable.

• a (float) – Intercept value of the square root model a + b * sqrt(x).

• b (float) – Scaling parameter of independent variable in the square root model a + b * sqrt(x).

Returns:

y_hat – Predicted y values.

Return type:

np.ndarray