visualize.one_animation

gen_animation_gss(func, bounds, history, **kwargs)

Generates an animation of the golden-section search on func between the bounds

Parameters:

• func (Callable) – callable that depends on the first positional argument

• bounds (tuple[float, float]) – tuple with left and right points on the x-axis

• history (HistoryGSS) – a history object. a dict with lists. keys iteration, f_value, middle_point, left_point, right_point

Returns:

go.Figure with graph

Return type:

Figure

>>> def f(x): return x ** 3 - x ** 2 - x
>>> _, h = golden_section_search(f, (0, 2), keep_history=True)
>>> gen_animation_gss(f, (0, 2), h)

gen_animation_spi(func, bounds, history)

Generate animation. Per each iteration we create a go.Frame with parabola plot passing through three points

Parameters:

• history (HistorySPI) – a history object. a dict with lists. keys iteration, f_value, middle_point, left_point, right_point

• bounds ([float, float]) – tuple with left and right points on the x-axis

• func (Callable[[float], float]) – the functions for which the story was created

Return type:

go.Figure

>>> def f(x): return x ** 3 - x ** 2 - x
>>> _, h = successive_parabolic_interpolation(f, (0, 2), keep_history=True)
>>> gen_animation_spi(f, (0, 2), h)

gen_animation_brent(func, history)

Returns a visualization of the Brent algorithm. Each iteration shows which iteration.

Parameters:

• func (Callable[[float], float]) – callable that depends on the first positional argument

• history (HistoryBrent) – brent optimization history

Returns:

animation of optimization

Return type:

Figure

>>> def f(x): return x ** 3 - x ** 2 - x
>>> _, h = brent(f, (0, 2), keep_history=True)
>>> gen_animation_brent(f, h)

parabolic_coefficients(x0, x1, x2, func)

Returns a parabolic function passing through the specified points x0, x1, x2 coeficients

>>> parabolic_coefficients(0, 1, 2, lambda x: x ** 2)
(1.0, 0.0, 0.0)

(1)\[\begin{bmatrix} a \\ b \\ c \end{bmatrix} = \begin{bmatrix} x_0^2 & x_0 & 1 \\ x_1^2 & x_1 & 1 \\ x_2^2 & x_2 & 1 \end{bmatrix}^{-1} \cdot \begin{bmatrix} y_0 \\ y_1 \\ y_2 \end{bmatrix}\]

Parameters:

• x0 (float) – first point

• x1 (float) – second point

• x2 (float) – third point

• func (Callable[[float], float]) – the functions for which the story was created

Returns:

coefficients of the parabolic function

Return type:

[float, float, float]

transfer_history_gss(history, func)

Generate data for plotly express with using animation_frame for animate

>>> def f(x): return x ** 2
>>> _, hist = golden_section_search(f, (-1, 2), keep_history=True)
>>> data_for_plot = transfer_history_gss(hist, f)
Searching finished. Successfully. code 0

>>> data_for_plot[::30]

	iteration	type	x	y	size
0	0	middle	0.500000	0.250000	3
30	4	left	-0.291796	0.085145	3
60	8	right	0.042572	0.001812	3

Parameters:

• history (HistoryGSS) – a history object. a dict with lists. keys iteration, f_value, middle_point, left_point, right_point

• func – the functions for which the story was created

Returns:

pd.DataFrame for gen_animation_gss. index - num of iteration.

Return type:

DataFrame