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After running 2to3

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This commit is contained in:
Grant Sanderson
2018-08-09 17:56:05 -07:00
parent 06a65190e7
commit 858051a806
172 changed files with 2117 additions and 2221 deletions
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86
old_projects/nn/part3.py
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@ -146,7 +146,7 @@ class InterpretGradientComponents(GradientNudging):
self.play(randy.change, "confused", words)
self.play(Blink(randy))
self.wait()
self.play(*map(FadeOut, [randy, words, arrow]))
self.play(*list(map(FadeOut, [randy, words, arrow])))
def circle_magnitudes(self):
rects = VGroup()
@ -286,9 +286,9 @@ class InterpretGradientComponents(GradientNudging):
run_time = 2,
)
self.wait()
self.play(*map(FadeOut, [
self.play(*list(map(FadeOut, [
w.dot, w.brace, w.number_line, w.words
]))
])))
######
@ -501,7 +501,7 @@ class ShowAveragingCost(PreviewLearning):
words.scale(0.8)
words.to_corner(UP+LEFT)
for x in xrange(self.n_adjustments):
for x in range(self.n_adjustments):
if x < 2:
self.play(FadeIn(words[x]))
for train_in, train_out in training_data[:n_examples_per_adjustment]:
@ -660,7 +660,7 @@ class WalkThroughTwoExample(ShowAveragingCost):
self.play(
FadeOut(to_fade),
*map(MoveToTarget, movers)
*list(map(MoveToTarget, movers))
)
self.show_decimals(neurons)
self.cannot_directly_affect_activations()
@ -1031,9 +1031,9 @@ class WalkThroughTwoExample(ShowAveragingCost):
two_neuron_arrow.next_to(two_neuron, UP)
VGroup(neuron_arrows, two_neuron_arrow).set_color(YELLOW)
neuron_rects = VGroup(*map(
neuron_rects = VGroup(*list(map(
SurroundingRectangle, bright_neurons
))
)))
two_neuron_rect = SurroundingRectangle(two_neuron)
seeing_words = TextMobject("Seeing a 2")
seeing_words.scale(0.8)
@ -1260,7 +1260,7 @@ class WalkThroughTwoExample(ShowAveragingCost):
arrows_to_fade = VGroup(prev_neuron_arrows)
output_labels = self.network_mob.output_labels
quads = zip(neurons, self.decimals, self.arrows, output_labels)
quads = list(zip(neurons, self.decimals, self.arrows, output_labels))
self.revert_to_original_skipping_status()
self.play(
@ -1498,9 +1498,9 @@ class ConstructGradientFromAllTrainingExamples(Scene):
self.v_lines = v_lines
def setup_weights(self):
weights = VGroup(*map(TexMobject, [
weights = VGroup(*list(map(TexMobject, [
"w_0", "w_1", "w_2", "\\vdots", "w_{13{,}001}"
]))
])))
for i, weight in enumerate(weights):
weight.move_to(self.get_grid_position(i, 0))
weights.to_edge(LEFT, buff = MED_SMALL_BUFF)
@ -1520,7 +1520,7 @@ class ConstructGradientFromAllTrainingExamples(Scene):
self.add(two)
self.two_changes = VGroup()
for i in range(3) + [4]:
for i in list(range(3)) + [4]:
weight = self.weights[i]
bubble, change = self.get_requested_change_bubble(two)
weight.save_state()
@ -1591,7 +1591,7 @@ class ConstructGradientFromAllTrainingExamples(Scene):
self.get_random_decimal().move_to(
self.get_grid_position(i, j)
)
for i in range(3) + [4]
for i in list(range(3)) + [4]
for j in range(1, self.n_examples)
])
for change in changes:
@ -1624,7 +1624,7 @@ class ConstructGradientFromAllTrainingExamples(Scene):
VGroup(two_change, *changes[k*i:k*(i+1)])
for i, two_change in enumerate(self.two_changes)
])
for i in range(3) + [-1]:
for i in list(range(3)) + [-1]:
self.change_rows[i].add(more_h_dots[i])
self.all_eyes = VGroup(*[
@ -2212,10 +2212,7 @@ class SimplestNetworkExample(PreviewLearning):
w.set_color(edge.get_color())
b_terms = expression.get_parts_by_tex("b_")
variables = VGroup(*it.chain(w_terms, b_terms))
other_terms = VGroup(*filter(
lambda m : m not in variables,
expression
))
other_terms = VGroup(*[m for m in expression if m not in variables])
random.shuffle(variables.submobjects)
self.play(ReplacementTransform(edges.copy(), w_terms))
@ -2238,10 +2235,10 @@ class SimplestNetworkExample(PreviewLearning):
self.remove(expression)
def focus_just_on_last_two_layers(self):
to_fade = VGroup(*it.chain(*zip(
to_fade = VGroup(*it.chain(*list(zip(
self.network_mob.layers[:2],
self.network_mob.edge_groups[:2],
)))
))))
for mob in to_fade:
mob.save_state()
self.play(LaggedStart(
@ -2316,7 +2313,7 @@ class SimplestNetworkExample(PreviewLearning):
Write(not_exponents, run_time = 2)
)
self.wait()
self.play(*map(FadeOut, [not_exponents, superscript_rects]))
self.play(*list(map(FadeOut, [not_exponents, superscript_rects])))
self.set_variables_as_attrs(
a_labels, a_label_arrows, decimals,
@ -2343,7 +2340,7 @@ class SimplestNetworkExample(PreviewLearning):
)
VGroup(words, rect, y_label).set_color(self.desired_output_color)
self.play(*map(FadeIn, [neuron, decimal]))
self.play(*list(map(FadeIn, [neuron, decimal])))
self.play(
ShowCreation(rect),
Write(words, run_time = 1)
@ -2391,9 +2388,7 @@ class SimplestNetworkExample(PreviewLearning):
"(", "0.00", "-", "0.00", ")", "^2"
)
numbers = expression.get_parts_by_tex("0.00")
non_numbers = VGroup(*filter(
lambda m : m not in numbers, expression
))
non_numbers = VGroup(*[m for m in expression if m not in numbers])
expression.next_to(cost_equation, DOWN, aligned_edge = RIGHT)
decimals = VGroup(
self.decimals[0],
@ -2409,10 +2404,7 @@ class SimplestNetworkExample(PreviewLearning):
ReplacementTransform(pre_y, y),
)
self.play(LaggedStart(
FadeIn, VGroup(*filter(
lambda m : m not in [a, y],
cost_equation
))
FadeIn, VGroup(*[m for m in cost_equation if m not in [a, y]])
))
self.play(
MoveToTarget(decimals),
@ -2425,7 +2417,7 @@ class SimplestNetworkExample(PreviewLearning):
)
self.play(C0.shift, MED_SMALL_BUFF*UP, rate_func = wiggle)
self.wait()
self.play(*map(FadeOut, [decimals, non_numbers]))
self.play(*list(map(FadeOut, [decimals, non_numbers])))
self.set_variables_as_attrs(
cost_equation, cost_word, cost_arrow
@ -2955,14 +2947,11 @@ class SimplestNetworkExample(PreviewLearning):
graph_parts.scale, 0.7, graph_parts.get_bottom()
)
self.wait(2)
self.play(*map(FadeOut, [rect, words]))
self.play(*list(map(FadeOut, [rect, words])))
def indicate_everything_on_screen(self):
everything = VGroup(*self.get_top_level_mobjects())
everything = VGroup(*filter(
lambda m : not m.is_subpath,
everything.family_members_with_points()
))
everything = VGroup(*[m for m in everything.family_members_with_points() if not m.is_subpath])
self.play(LaggedStart(
Indicate, everything,
rate_func = wiggle,
@ -3091,7 +3080,7 @@ class SimplestNetworkExample(PreviewLearning):
for d in (decimal, moving_decimals[0])
]
)
self.play(*map(FadeOut, [double_arrow, moving_decimals]))
self.play(*list(map(FadeOut, [double_arrow, moving_decimals])))
#da_dz
self.play(
@ -3292,11 +3281,11 @@ class SimplestNetworkExample(PreviewLearning):
)
self.wait(2)
self.cycle_through_altnernate_training_examples()
self.play(*map(FadeOut, [
self.play(*list(map(FadeOut, [
VGroup(*full_derivative[3:]),
lhs_brace, lhs_text,
rhs_brace, rhs_text,
]))
])))
self.dC_dw = lhs
@ -3472,7 +3461,7 @@ class SimplestNetworkExample(PreviewLearning):
)
)
self.wait(2)
self.play(*map(FadeOut, [dz_db, arrow, one]))
self.play(*list(map(FadeOut, [dz_db, arrow, one])))
self.dz_db = dz_db
@ -3726,7 +3715,7 @@ class GeneralFormulas(SimplestNetworkExample):
all_subscript_rects.add(subscript_rects)
start_labels, start_arrows = [
VGroup(*map(VGroup, [group[i][0] for i in (0, 1)])).copy()
VGroup(*list(map(VGroup, [group[i][0] for i in (0, 1)]))).copy()
for group in (all_labels, all_arrows)
]
for label in start_labels:
@ -3734,7 +3723,7 @@ class GeneralFormulas(SimplestNetworkExample):
self.add(all_decimals)
self.play(*it.chain(
map(Write, start_labels),
list(map(Write, start_labels)),
[GrowArrow(a[0]) for a in start_arrows]
))
self.wait()
@ -3824,7 +3813,7 @@ class GeneralFormulas(SimplestNetworkExample):
*[
ReplacementTransform(n1.decimal.copy(), n2.decimal)
for n1, n2 in zip(layer.neurons, desired_output.neurons)
] + map(GrowArrow, arrows)
] + list(map(GrowArrow, arrows))
)
self.wait()
@ -3848,10 +3837,7 @@ class GeneralFormulas(SimplestNetworkExample):
aj.target = cost_equation.get_part_by_tex("a^{(L)}_j")
yj.target = cost_equation.get_part_by_tex("y_j")
yj.target.set_color(self.desired_output_color)
to_fade_in = VGroup(*filter(
lambda m : m not in [aj.target, yj.target],
cost_equation
))
to_fade_in = VGroup(*[m for m in cost_equation if m not in [aj.target, yj.target]])
sum_part = cost_equation.get_part_by_tex("sum")
self.play(*[
@ -3874,10 +3860,7 @@ class GeneralFormulas(SimplestNetworkExample):
def show_example_weight(self):
edges = self.network_mob.edge_groups[-1]
edge = self.chosen_neurons[1].edges_in[1]
faded_edges = VGroup(*filter(
lambda e : e is not edge,
edges
))
faded_edges = VGroup(*[e for e in edges if e is not edge])
faded_edges.save_state()
for faded_edge in faded_edges:
faded_edge.save_state()
@ -3974,10 +3957,7 @@ class GeneralFormulas(SimplestNetworkExample):
aLm1_part
),
)
self.play(Write(VGroup(*filter(
lambda m : m not in [w_part, aLm1_part],
z_formula
))))
self.play(Write(VGroup(*[m for m in z_formula if m not in [w_part, aLm1_part]])))
self.wait()
self.play(ReplacementTransform(
self.chosen_neurons[1].label.copy(),