Try np.meshgrid() instead of mgrid:
X, Y, Z = np.meshgrid(x, y, z)
AttributeError: 'numpy.ndarray' object has no attribute 'numpy'
scikit learn - AttributeError: 'numpy.ndarray' object has no attribute 'fit' - Data Science Stack Exchange
python - AttributeError: 'numpy.ndarray' object has no attribute 'predict' - Data Science Stack Exchange
AttributeError: 'numpy.ndarray' object has no attribute 'dim'
Hello there.
I started doing manim a few weeks ago and, since there's no official documentation, I've been trying to replicate some of the scenes of 3b1b's old proyects. Specifically, I'm trying to replicate this animation ( https://www.youtube.com/watch?v=k7RM-ot2NWY at 0:37), but whenever I try to render the file, this error appears: "AttributeError: 'numpy.ndarray' object has no attribute 'move_to'".
I've already spend hours looking through the manimlib files but i can't make sense out of this.
Heres the code of the animation that I've got so far:
from manimlib.imports import *
class VectoresComoEscalares(VectorScene):
CONFIG = {
"axis_config": {
"stroke_color": WHITE,
"stroke_width": 2,
"stroke_opacity": 0.8,
},
"background_line_style": {"stroke_width": 2, "stroke_opacity": 0.5,},
"vcoords": ([3, 2]),
"vcolor": ORANGE,
}
def construct(self):
grid = NumberPlane(**self.CONFIG)
self.add(grid)
self.lock_in_faded_grid()
self.play(ShowCreation(grid), run_time=2)
self.wait()
kwargs = {
"stroke_width": 4,
}
vector = self.add_vector(self.vcoords, self.vcolor, **kwargs)
array, x_line, y_line = self.vector_to_coords(vector)
self.add(array)
self.wait()
new_array = self.ideas_generales_escalares(array, vector)
self.scale_basis_vectors(new_array)
self.show_symbolic_sum(new_array, vector)
def ideas_generales_escalares(self, array, vector):
startmo = self.get_mobjects()
txt = TextMobject(
"Vean cada coordenada como un escalar.", tex_to_color_map={"escalar": RED}
)
txt.to_edge(DOWN)
x, y = array.get_entries()
new_x = x.copy().scale(2).set_color(X_COLOR)
new_x.move_to(3 * LEFT + 2 * UP)
new_y = y.copy().scale(2).set_color(Y_COLOR)
new_y.move_to(3 * RIGHT + 2 * UP)
i_hat, j_hat = self.get_basis_vectors()
new_i_hat = Vector(self.vcoords[0] * i_hat.get_end(), color=X_COLOR)
new_j_hat = Vector(self.vcoords[1] * j_hat.get_end(), color=X_COLOR)
g1 = VGroup(i_hat, new_i_hat).shift(3 * LEFT)
g2 = VGroup(i_hat, new_j_hat).shift(3 * RIGHT)
new_array = Matrix([new_x.copy(), new_y.copy()])
new_array.scale(0.5)
new_array.shift(
-new_array.get_boundary_point(-vector.get_end()) + 1.1 * vector.get_end()
)
self.remove(*startmo)
self.play(Transform(x, new_x), Transform(y, new_y), Write(txt))
self.play(FadeIn(i_hat), FadeIn(j_hat))
self.wait()
self.play(FadeIn(i_hat), FadeIn(j_hat))
self.wait()
self.play(Transform(i_hat, new_i_hat), Transform(j_hat, new_j_hat), run_time=3)
self.wait()
startmo.remove(array)
new_x, new_y = new_array.get_entries()
self.play(
Transform(x, new_x),
Transform(y, new_y),
FadeOut(i_hat),
FadeOut(j_hat),
Write(new_array.get_brackets()),
FadeIn(VMobject(*startmo)),
FadeOut(txt),
)
self.remove(x, y)
self.add(new_array)
return new_array
It would be helpful if you could post the full stack trace, so that we can see which line your error occurs at. In general, the more information you can provide in a question, the better.
In this case, it looks like your full_model_pipeline may somehow become a numpy array. Since you have a one-element pipeline, you could try changing
full_model_pipeline = Pipeline(steps =[
('full_pipeline',full_pipeline),
('model',LinearRegression())
])
full_model_pipeline.fit(X_train,y_train)
to
model = LinearRegression()
model.fit(X_train, y_train)
I believe you need to add () where you add scaler to the pipeline: ('std_scaler',StandardScaler) --> ('std_scaler',StandardScaler())
Hi,
I'm trying to create a numpy v-stack and creating 3 np.array's for it, by filling them with a loop:
I get the error: 'AttributeError: 'numpy.ndarray' object has no attribute 'np' . I think I'm using the wrong notation to append to the empty arrays:
neighbor_id = [id_ for id_ in range(1, n_obs) if id_ != user_id]
neighbor_id_arr = np.array(neighbor_id)
similarity = np.array([])
num_interactions = np.array([])
# get similarity and num_interactions
for id_ in neighbor_id:
similarity.np.append(np.dot(user_item.loc[user_id],user_item.loc[id_])) #The issue is here, I think
num_interactions.np.append(user_interactions.loc[id_])
c = numpy.vstack((neighbor_id_arr, similarity,num_interactions))
Thanks!
James
https://imgur.com/gallery/yAdAjdx
Hello,
Linked is the screenshot of the two error messages I keep recieving as well as the code leading up to it. I'm trying to run an uplift on a classification tree. Any help is appreciated, I am still fairly new to python. Thank you!!