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Random fixes to old_projects

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This commit is contained in:
Grant Sanderson
2018-08-12 00:35:15 -07:00
parent 535522d86b
commit 6ab8f7f7fc
9 changed files with 24 additions and 19 deletions
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1
old_projects/WindingNumber_G.py
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@ -2351,6 +2351,7 @@ class TransitionFromPathsToBoundariesArrowless(TransitionFromPathsToBoundaries):
class BreakDownLoopWithNonzeroWinding(TransitionFromPathsToBoundaries):
def construct(self):
TransitionFromPathsToBoundaries.construct(self)
zero_point = 2*LEFT
squares, joint_rect = self.get_squares_and_joint_rect()

2
old_projects/brachistochrone/cycloid.py
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@ -337,7 +337,7 @@ class LeviSolution(CycloidScene):
new_theta.next_to(new_arc, LEFT)
new_theta.shift(0.1*DOWN)
kwargs = {
"stroke_width" : 2*DEFAULT_POINT_THICKNESS,
"stroke_width" : 2*DEFAULT_STROKE_WIDTH,
}
triangle1 = Polygon(
self.p_point, self.c_point, self.bottom_point,

2
old_projects/complex_multiplication_article.py
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@ -9,7 +9,7 @@ from big_ol_pile_of_manim_imports import *
from functools import reduce
DEFAULT_PLANE_CONFIG = {
"stroke_width" : 2*DEFAULT_POINT_THICKNESS
"stroke_width" : 2*DEFAULT_STROKE_WIDTH
}

14
old_projects/matrix_as_transform_2d.py
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@ -7,7 +7,7 @@ import sys
from big_ol_pile_of_manim_imports import *
ARROW_CONFIG = {"stroke_width" : 2*DEFAULT_POINT_THICKNESS}
ARROW_CONFIG = {"stroke_width" : 2*DEFAULT_STROKE_WIDTH}
LIGHT_RED = RED_E
def matrix_to_string(matrix):
@ -52,7 +52,7 @@ class ShowMultiplication(NumberLineScene):
def construct(self, num, show_original_line):
config = {
"density" : max(abs(num), 1)*DEFAULT_POINT_DENSITY_1D,
"stroke_width" : 2*DEFAULT_POINT_THICKNESS
"stroke_width" : 2*DEFAULT_STROKE_WIDTH
}
if abs(num) < 1:
config["numerical_radius"] = FRAME_X_RADIUS/num
@ -115,7 +115,7 @@ class ExamplesOfNonlinearOneDimensionalTransforms(NumberLineScene):
self.clear()
self.add(self.nonlinear)
config = {
"stroke_width" : 2*DEFAULT_POINT_THICKNESS,
"stroke_width" : 2*DEFAULT_STROKE_WIDTH,
"density" : 5*DEFAULT_POINT_DENSITY_1D,
}
NumberLineScene.construct(self, **config)
@ -144,7 +144,7 @@ class ShowTwoThenThree(ShowMultiplication):
def construct(self):
config = {
"stroke_width" : 2*DEFAULT_POINT_THICKNESS,
"stroke_width" : 2*DEFAULT_STROKE_WIDTH,
"density" : 6*DEFAULT_POINT_DENSITY_1D,
}
NumberLineScene.construct(self, **config)
@ -163,7 +163,7 @@ class TransformScene2D(Scene):
"x_radius" : FRAME_WIDTH,
"y_radius" : FRAME_WIDTH,
"density" : DEFAULT_POINT_DENSITY_1D*density_factor,
"stroke_width" : 2*DEFAULT_POINT_THICKNESS
"stroke_width" : 2*DEFAULT_STROKE_WIDTH
}
if not use_faded_lines:
config["x_faded_line_frequency"] = None
@ -323,7 +323,7 @@ class ExamplesOfNonlinearTwoDimensionalTransformations(Scene):
"x_radius" : FRAME_WIDTH,
"y_radius" : FRAME_WIDTH,
"density" : 3*DEFAULT_POINT_DENSITY_1D,
"stroke_width" : 2*DEFAULT_POINT_THICKNESS
"stroke_width" : 2*DEFAULT_STROKE_WIDTH
}
number_plane = NumberPlane(**config)
numbers = number_plane.get_coordinate_labels()
@ -377,7 +377,7 @@ class TrickyExamplesOfNonlinearTwoDimensionalTransformations(Scene):
"x_radius" : 0.6*FRAME_WIDTH,
"y_radius" : 0.6*FRAME_WIDTH,
"density" : 10*DEFAULT_POINT_DENSITY_1D,
"stroke_width" : 2*DEFAULT_POINT_THICKNESS
"stroke_width" : 2*DEFAULT_STROKE_WIDTH
}
number_plane = NumberPlane(**config)
phrase1, phrase2 = TextMobject([

BIN
old_projects/nn/image_map
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Binary file not shown.

6
old_projects/nn/mnist_loader.py
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@ -40,7 +40,11 @@ def load_data():
below.
"""
f = gzip.open('/Users/grant/cs/neural-networks-and-deep-learning/data/mnist.pkl.gz', 'rb')
training_data, validation_data, test_data = pickle.load(f)
u = pickle._Unpickler(f)
u.encoding = 'latin1'
# p = u.load()
# training_data, validation_data, test_data = pickle.load(f)
training_data, validation_data, test_data = u.load()
f.close()
return (training_data, validation_data, test_data)

12
old_projects/nn/network.py
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@ -165,8 +165,8 @@ class Network(object):
return sum(int(x == y) for (x, y) in test_results)
def cost_derivative(self, output_activations, y):
"""Return the vector of partial derivatives \partial C_x /
\partial a for the output activations."""
"""Return the vector of partial derivatives \\partial C_x /
\\partial a for the output activations."""
return (output_activations-y)
#### Miscellaneous functions
@ -195,8 +195,8 @@ def ReLU_prime(z):
return (np.array(z) > 0).astype('int')
def get_pretrained_network():
data_file = open(PRETRAINED_DATA_FILE)
weights, biases = pickle.load(data_file)
data_file = open(PRETRAINED_DATA_FILE, 'rb')
weights, biases = pickle.load(data_file, encoding='latin1')
sizes = [w.shape[1] for w in weights]
sizes.append(weights[-1].shape[0])
network = Network(sizes)
@ -275,13 +275,13 @@ def save_organized_images(n_images_per_number = 10):
if len(image_map[value]) >= n_images_per_number:
continue
image_map[value].append(im)
data_file = open(IMAGE_MAP_DATA_FILE, mode = 'w')
data_file = open(IMAGE_MAP_DATA_FILE, mode = 'wb')
pickle.dump(image_map, data_file)
data_file.close()
def get_organized_images():
data_file = open(IMAGE_MAP_DATA_FILE, mode = 'r')
image_map = pickle.load(data_file)
image_map = pickle.load(data_file, encoding='latin1')
data_file.close()
return image_map

4
old_projects/nn/part1.py
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@ -144,10 +144,10 @@ class NetworkMobject(VGroup):
if size > n_neurons:
dots = TexMobject("\\vdots")
dots.move_to(neurons)
VGroup(*neurons[:len(neurons)/2]).next_to(
VGroup(*neurons[:len(neurons) // 2]).next_to(
dots, UP, MED_SMALL_BUFF
)
VGroup(*neurons[len(neurons)/2:]).next_to(
VGroup(*neurons[len(neurons) // 2:]).next_to(
dots, DOWN, MED_SMALL_BUFF
)
layer.dots = dots

2
old_projects/wcat.py
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@ -453,7 +453,7 @@ class DefineInscribedSquareProblem(ClosedLoopScene):
pi_loop.set_fill(opacity = 0)
pi_loop.set_stroke(
color = WHITE,
width = DEFAULT_POINT_THICKNESS
width = DEFAULT_STROKE_WIDTH
)
pi_loop.set_height(4)
randy = Randolph()