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April 4, 2025 15:35
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Two-part logistic
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import altair as alt | |
import numpy as np | |
import polars as pl | |
import streamlit as st | |
def logistic(x, y0, scale, x0, k): | |
return y0 + scale / (1 + np.exp(-k * (x - x0))) | |
def app(): | |
st.title("My curve") | |
k_left = st.slider("k_left", 0.0, 5.0, 1.0) | |
scale_left = st.slider("scale_left", 0.0, 1.0, 0.25) | |
scale_right = st.slider("scale_right", 0.0, 3.0, 0.75) | |
x0 = 0.0 | |
a1 = 0.0 | |
a2 = (scale_left - scale_right) / 2.0 | |
k_right = k_left * scale_left / scale_right | |
x = np.linspace(-10, 10, 1001) | |
y_left = logistic(x, y0=a1, scale=scale_left, x0=x0, k=k_left) | |
y_right = logistic(x, y0=a2, scale=scale_right, x0=x0, k=k_right) | |
y = np.where(x < x0, y_left, y_right) | |
df = pl.DataFrame({"x": x, "y": y}) | |
chart = ( | |
alt.Chart(df) | |
.mark_line() | |
.encode( | |
x=alt.X("x", title="x"), | |
y=alt.Y("y", title="y", scale=alt.Scale(domain=(0.0, 1.0))), | |
) | |
) | |
st.altair_chart(chart, use_container_width=True) | |
st.header("Math") | |
st.markdown( | |
r""" | |
Each half $i \in \{1, 2\}$ of the curve follows this form: | |
$$ | |
f_i(x; a_i, L_i, x_0, k_i) = a_i + \frac{L_i}{1 + e^{-k_i (x - x_0)}} | |
$$ | |
with a combined curve: | |
$$ | |
f(x) = \begin{cases} | |
f_1(x; a_1, L_1, x_0, k_1) & \text{if } x < x_0 \\ | |
f_2(x; a_2, L_2, x_0, k_2) & \text{if } x \geq x_0 | |
\end{cases} | |
$$ | |
We immediately set $a_1 = 0$. To ensure continuity, we require: | |
$$ | |
\begin{align*} | |
f_1(x_0) &= f_2(x_0) \\ | |
a_1 + \frac{1}{2} L_1 &= a_2 + \frac{1}{2} L_2 \\ | |
\frac{1}{2} (L_1 - L_2) &= a_2 | |
\end{align*} | |
$$ | |
And also: | |
$$ | |
\begin{align*} | |
f_1'(x_0) &= f_2'(x_0) \\ | |
\frac{1}{4} k_1 L_1 &= \frac{1}{4} k_2 L_2 \\ | |
k_1 \frac{L_1}{L_2} &= k_2 | |
\end{align*} | |
$$ | |
This leaves free params: $x_0$, $L_1$, $L_2$, and $k_1$. | |
""" | |
) | |
if __name__ == "__main__": | |
app() |
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