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calesthioandHanzo Dev
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11 production pipelines, 47 tools, 124 agent skills. Supports cloud APIs (fal.ai, OpenAI, ElevenLabs, Suno, HeyGen, Runway) and free local providers (diffusers, Piper TTS, WAN 2.1, Hunyuan, CogVideo). Co-authored-by: Hanzo Dev <[email protected]>
136 lines
3.8 KiB
Python
136 lines
3.8 KiB
Python
"""
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Linear Regression visualization showing data points and a fitted line.
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Demonstrates: Axes, DotCloud, Line, ValueTracker, updaters
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"""
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from manimlib import *
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import numpy as np
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import random
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class LinearRegression(Scene):
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def construct(self):
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# Set up axes
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x_min, x_max = (-1, 12)
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y_min, y_max = (-1, 10)
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axes = Axes((x_min, x_max), (y_min, y_max), width=12, height=6)
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axes.to_edge(DOWN)
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self.add(axes)
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# Add data points
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n_data_points = 30
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m = 0.75 # slope
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y0 = 1 # y-intercept
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np.random.seed(42)
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data = np.array([
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(x, y0 + m * x + 0.75 * np.random.normal(0, 1))
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for x in np.random.uniform(2, x_max, n_data_points)
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])
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points = axes.c2p(data[:, 0], data[:, 1])
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dots = DotCloud(points)
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dots.set_color(YELLOW)
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dots.set_glow_factor(1)
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dots.set_radius(0.075)
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self.add(dots)
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# Title
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title = Text("Linear Regression", font_size=72)
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title.to_edge(UP)
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# Create line with trackers for slope and y-intercept
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m_tracker = ValueTracker(m)
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y0_tracker = ValueTracker(y0)
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line = Line()
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line.set_stroke(TEAL, 2)
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def update_line(line):
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curr_y0 = y0_tracker.get_value()
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curr_m = m_tracker.get_value()
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line.put_start_and_end_on(
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axes.c2p(0, curr_y0),
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axes.c2p(x_max, curr_y0 + curr_m * x_max),
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)
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line.add_updater(update_line)
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self.play(
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FadeIn(title, UP),
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ShowCreation(line),
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)
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self.wait()
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# Label inputs and outputs
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in_label = Text("Input")
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in_label.next_to(axes.x_axis, DOWN, buff=0.1, aligned_edge=RIGHT)
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out_label = Text("Output")
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out_label.rotate(90 * DEGREES)
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out_label.next_to(axes.y_axis, LEFT, aligned_edge=UP)
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self.play(LaggedStart(
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FadeIn(in_label, lag_ratio=0.1),
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FadeIn(out_label, lag_ratio=0.1),
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lag_ratio=0.5,
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))
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self.wait()
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# Emphasize line
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self.play(
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VShowPassingFlash(
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line.copy().set_stroke(BLUE, 8).scale(1.1).insert_n_curves(100),
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time_width=1.5,
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run_time=2
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),
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)
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self.wait()
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# Show parameter labels
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m_label = VGroup(
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Text("slope = "),
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DecimalNumber(m_tracker.get_value()),
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)
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m_label.arrange(RIGHT)
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m_label[1].f_always.set_value(m_tracker.get_value)
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y0_label = VGroup(
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Text("y-intercept = "),
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DecimalNumber(y0_tracker.get_value()),
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)
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y0_label.arrange(RIGHT)
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y0_label[1].f_always.set_value(y0_tracker.get_value)
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labels = VGroup(m_label, y0_label)
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labels.arrange(DOWN, aligned_edge=LEFT)
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labels.next_to(axes.y_axis, RIGHT, buff=1.0)
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labels.to_edge(UP)
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self.play(
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FadeOut(title, UP),
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FadeIn(m_label, UP),
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)
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self.play(
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m_tracker.animate.set_value(1.5),
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run_time=2,
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)
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self.play(FadeIn(y0_label, UP))
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self.play(
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y0_tracker.animate.set_value(-2),
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run_time=2
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)
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self.wait()
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# Tweak line parameters to show fitting
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for n in range(6):
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alpha = random.random()
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if alpha > 0.5:
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alpha += 1
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new_m = interpolate(m_tracker.get_value(), m, alpha)
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new_y0 = interpolate(y0_tracker.get_value(), y0, alpha)
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self.play(LaggedStart(
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m_tracker.animate.set_value(new_m),
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y0_tracker.animate.set_value(new_y0),
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run_time=1.5,
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lag_ratio=0.25,
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))
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self.wait(0.5)
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