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About to describe weights in NN
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Grant Sanderson
parent
72981185a4
commit
45ac45e259
2 changed files with 135 additions and 16 deletions
14
constants.py
14
constants.py
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@ -47,13 +47,13 @@ DEFAULT_POINTWISE_FUNCTION_RUN_TIME = 3.0
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DEFAULT_DITHER_TIME = 1.0
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ORIGIN = np.array(( 0, 0, 0))
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UP = np.array(( 0, 1, 0))
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DOWN = np.array(( 0,-1, 0))
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RIGHT = np.array(( 1, 0, 0))
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LEFT = np.array((-1, 0, 0))
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IN = np.array(( 0, 0,-1))
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OUT = np.array(( 0, 0, 1))
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ORIGIN = np.array(( 0., 0., 0.))
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UP = np.array(( 0., 1., 0.))
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DOWN = np.array(( 0.,-1., 0.))
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RIGHT = np.array(( 1., 0., 0.))
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LEFT = np.array((-1., 0., 0.))
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IN = np.array(( 0., 0.,-1.))
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OUT = np.array(( 0., 0., 1.))
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TOP = SPACE_HEIGHT*UP
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BOTTOM = SPACE_HEIGHT*DOWN
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137
nn/scenes.py
137
nn/scenes.py
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@ -1,5 +1,6 @@
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import sys
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import os.path
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import cv2
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from helpers import *
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@ -40,13 +41,13 @@ from nn.network import *
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DEFAULT_GAUSS_BLUR_CONFIG = {
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"ksize" : (5, 5),
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"sigmaX" : 6,
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"sigmaY" : 6,
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"sigmaX" : 10,
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"sigmaY" : 10,
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}
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DEFAULT_CANNY_CONFIG = {
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"threshold1" : 50,
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"threshold2" : 100,
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"threshold1" : 100,
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"threshold2" : 200,
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}
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@ -643,6 +644,7 @@ class LayOutPlan(TeacherStudentsScene, NetworkScene):
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*["confused"]*3,
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submobject_mode = "all_at_once"
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),
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self.teacher.change, "plain",
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run_time = 1
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)
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self.play(ShowCreation(
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@ -652,10 +654,8 @@ class LayOutPlan(TeacherStudentsScene, NetworkScene):
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lag_factor = 8,
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rate_func = None,
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))
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self.play(self.teacher.change, "plain")
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in_vect = np.random.random(self.network.sizes[0])
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self.feed_forward(in_vect)
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self.dither()
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def show_math(self):
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equation = TexMobject(
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@ -673,6 +673,8 @@ class LayOutPlan(TeacherStudentsScene, NetworkScene):
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self.play(Write(equation, run_time = 2))
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self.dither()
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self.equation = equation
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def ask_about_layers(self):
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self.student_says(
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"Why the layers?",
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@ -683,18 +685,28 @@ class LayOutPlan(TeacherStudentsScene, NetworkScene):
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self.play(RemovePiCreatureBubble(self.students[2]))
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def show_learning(self):
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rect = SurroundingRectangle(self.words[0][1], color = YELLOW)
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word = self.words[0][1].copy()
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rect = SurroundingRectangle(word, color = YELLOW)
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self.network_mob.neuron_fill_color = YELLOW
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layer = self.network_mob.layers[-1]
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activation = np.zeros(len(layer))
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activation = np.zeros(len(layer.neurons))
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activation[1] = 1.0
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active_layer = self.network_mob.get_active_layer(
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-1, activation
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)
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word_group = VGroup(word, rect)
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word_group.generate_target()
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word_group.target.move_to(self.equation, LEFT)
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word_group.target[0].highlight(YELLOW)
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word_group.target[1].set_stroke(width = 0)
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self.play(ShowCreation(rect))
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self.play(Transform(layer, active_layer))
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self.play(
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Transform(layer, active_layer),
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FadeOut(self.equation),
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MoveToTarget(word_group),
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)
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for edge_group in reversed(self.network_mob.edge_groups):
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edge_group.generate_target()
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for edge in edge_group.target:
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@ -1914,6 +1926,113 @@ class SecondLayerIsLittleEdgeLayer(IntroduceEachLayer):
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run_time = 2,
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))
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class EdgeDetection(Scene):
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CONFIG = {
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"camera_config" : {"background_alpha" : 255}
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}
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def construct(self):
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lion = ImageMobject("Lion")
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edges_array = get_edges(lion.pixel_array)
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edges = ImageMobject(edges_array)
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group = Group(lion, edges)
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group.scale_to_fit_height(4)
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group.arrange_submobjects(RIGHT)
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lion_copy = lion.copy()
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self.play(FadeIn(lion))
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self.play(lion_copy.move_to, edges)
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self.play(Transform(lion_copy, edges, run_time = 3))
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self.dither(2)
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class ManyTasksBreakDownLikeThis(TeacherStudentsScene):
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def construct(self):
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audio = self.get_wave_form()
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audio_label = TextMobject("Raw audio")
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letters = TextMobject(" ".join("recognition"))
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syllables = TextMobject("$\\cdot$".join([
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"re", "cog", "ni", "tion"
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]))
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word = TextMobject(
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"re", "cognition",
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arg_separator = ""
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)
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word[1].highlight(BLUE)
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arrows = VGroup()
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def get_arrow():
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arrow = Arrow(ORIGIN, RIGHT, color = BLUE)
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arrows.add(arrow)
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return arrow
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sequence = VGroup(
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audio, get_arrow(),
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letters, get_arrow(),
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syllables, get_arrow(),
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word
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)
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sequence.arrange_submobjects(RIGHT)
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sequence.scale_to_fit_width(2*SPACE_WIDTH - 1)
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sequence.to_edge(UP)
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audio_label.next_to(audio, DOWN)
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VGroup(audio, audio_label).highlight(YELLOW)
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audio.save_state()
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self.teacher_says(
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"Many", "recognition", "tasks\\\\",
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"break down like this"
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)
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self.change_student_modes(*["pondering"]*3)
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self.dither()
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content = self.teacher.bubble.content
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pre_word = content[1]
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content.remove(pre_word)
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audio.move_to(pre_word)
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self.play(
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self.teacher.bubble.content.fade, 1,
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ShowCreation(audio),
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pre_word.shift, MED_SMALL_BUFF, DOWN
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)
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self.dither(2)
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self.play(
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RemovePiCreatureBubble(self.teacher),
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audio.restore,
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FadeIn(audio_label),
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*[
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ReplacementTransform(
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m1, m2
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)
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for m1, m2 in zip(pre_word, letters)
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]
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)
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self.play(
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GrowFromPoint(arrows[0], arrows[0].get_start()),
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)
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self.dither()
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self.play(
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GrowFromPoint(arrows[1], arrows[1].get_start()),
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LaggedStart(FadeIn, syllables, run_time = 1)
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)
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self.dither()
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self.play(
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GrowFromPoint(arrows[2], arrows[2].get_start()),
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LaggedStart(FadeIn, word, run_time = 1)
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)
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self.dither()
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def get_wave_form(self):
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func = lambda x : abs(sum([
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(1./n)*np.sin((n+3)*x)
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for n in range(1, 5)
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]))
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result = VGroup(*[
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Line(func(x)*DOWN, func(x)*UP)
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for x in np.arange(0, 4, 0.1)
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])
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result.set_stroke(width = 2)
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result.arrange_submobjects(RIGHT, buff = MED_SMALL_BUFF)
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result.scale_to_fit_height(1)
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return result
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