manim_ml package#

Subpackages#

manim_ml.neural_network package

Submodules#

manim_ml.image module#

class manim_ml.image.GrayscaleImageMobject(numpy_image, height=2.3)#

Bases: manim.mobject.types.image_mobject.ImageMobject

Mobject for creating images in Manim from numpy arrays

animation_overrides = {<class 'manim.animation.creation.Create'>: <function GrayscaleImageMobject.create>}#

create(run_time=2)#

classmethod from_path(path, height=2.3)#: Loads image from path

class manim_ml.image.LabeledColorImage(image, color='#FC6255', label='Positive', stroke_width=5, font_size=24, buff=0.2)#

Bases: manim.mobject.mobject.Group

Labeled Color Image

animation_overrides = {}#

manim_ml.list_group module#

class manim_ml.list_group.ListGroup(*layers)#

Bases: manim.mobject.mobject.Mobject

Indexable Group with traditional list operations

add(item)#: Adds to end

animation_overrides = {}#

get(index)#: Gets item at index

index_of(item)#: Returns index of item if it exists

insert(index, item)#: Inserts item at index

remove(item)#: Removes first instance of item

remove_at_index(index)#: Removes item at index

remove_at_indices(indices)#: Removes items at indices

replace(index, item)#: Replaces item at index

set_z_index(z_index_value, family=True)#: Sets z index of all values in ListGroup

manim_ml.manifold module#

Visaulization of a latent Manifold

manim_ml.one_to_one_sync module#

Module for handling syncing two animations one to one. The goal here is to zip up two classes and their respective animations, and create a joint class with the same animations that runs the animations for both classes at the same time. This way we can connect two isomorphic views of the same concept and visualize them at the same time.

class manim_ml.one_to_one_sync.OneToOneSync#: Bases: object

manim_ml.probability module#

class manim_ml.probability.GaussianDistribution(axes, mean=None, cov=None, dist_theme='gaussian', color='#FF862F', **kwargs)#

Bases: manim.mobject.types.vectorized_mobject.VGroup

Object for drawing a Gaussian distribution

animation_overrides = {}#

compute_covariance_rotation_and_scale(covariance)#

construct_gaussian_distribution(mean, covariance, color='#FF862F', num_ellipses=4)#: Returns a 2d Gaussian distribution object with given mean and covariance

construct_simple_gaussian_ellipse(mean, covariance, color='#FF862F')#: Returns a 2d Gaussian distribution object with given mean and covariance