Linear#

Description#

The Linear class computes a linear transformation of each input value, defined by the equation \(y = \text{scale} \times x + \text{shift} \). This function is fundamental for adjusting the scale and location of data, often used in data preprocessing or custom activation functions in machine learning models.

Equation:

\[ f(x) = \text{scale} \cdot x + \text{shift} \]

Parameters:

  • scale (double): The scaling factor applied to the input.

  • shift (double): The value added to the scaled input to shift the output.

NaN handling: NaN values are not modified by this function.

NaN handling#

Policy: ignore. A NaN in any input at index t causes the function to skip that step: output at t is NaN and internal state is unchanged. Subsequent finite samples are processed as if step t had not occurred.

Examples#

Usage example#

import numpy as np
import plotly.graph_objects as go
from screamer import Linear

# Generate example data
data = np.linspace(-10, 10, 100)

# Create a Linear transformation with scale=2 and shift=3
linear = Linear(scale=2, shift=3)
linear_data = linear(data)

fig = go.Figure()
fig.add_trace(go.Scatter(y=data, mode='lines', name='Original Data'))
fig.add_trace(go.Scatter(y=linear_data, mode='lines', name='Linear Transformation (scale=2, shift=3)', line=dict(color='magenta')))

fig.update_layout(
    title="Linear Transformation",
    yaxis_title="Output",
    xaxis_title="Input",
    margin=dict(l=20, r=20, t=40, b=20)
)

fig.show()