It would be nice to see your train_generator code for clarity, but it does not seem to be a torch DataLoader. In this case, you should probably convert your arrays to tensors manually. There are several ways to do so:
torch.from_numpy(numpy_array)- for numpy arrays;torch.as_tensor(list)- for common lists and tuples;torch.tensor(array)should also work but the above ways will avoid copying the data when possible.
AttributeError: 'numpy.ndarray' object has no attribute 'device' Process finished with exit code 1
AttributeError: 'numpy.ndarray' object has no attribute 'numpy'
Error creating numpy v-stack, 'AttributeError: 'numpy.ndarray' object has no attribute 'np'
'numpy.ndarray' object has no attribute 'cuda'
Videos
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
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
The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns
If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.
selected_feat= X.columns[(sel.get_support())]
This will return a list of the columns kept by the feature selector.
If you wanted to see how many features were kept you can just run this:
sel.get_support().sum() # by default this will count 'True' as 1 and 'False' as 0
because this :
X = df.iloc[:,:24481].values
y = df.iloc[:, -1].values
you should remove .values or make extra X_col, y_col like that
X_col = df.iloc[:,:24481]
y_col = df.iloc[:, -1]