对于一个“普通”的Python类,我习惯于可以任意添加额外的属性。例如,我可以执行以下操作: # Create a class
class MyClass: pass
# Create an object of this class
my_object = MyClass()
# Add any attribute I want
my_object.my_new_attribute = "Hello world!"
# Now it has this attribute and I can use it:
print(my_object.my_new_attribute
以下函数基本上返回numpy.ndarray
def getimage(id):
img = self.coco.loadImgs(id)
I = io.imread(img['coco_url'])
return I #returns 'numpy.ndarray'
从main调用的getimage函数:
x = load.getimage(id).
x = torch.load(x)
引发错误:
'numpy.ndarray' object has no attribute 'seek'
我想用tfquaternion按四元数旋转一个向量。但是我得到了以下错误 AttributeError: 'numpy.ndarray' object has no attribute 'normalized'。 import tfquaternion as tfq
train_points = tfq.rotate_vector_by_quaternion(transf[:,3:],train_points)
我有一个numpy数组,它看起来像
a = ['blue' 'red' 'green']
我希望它能成为
b = ['blue', 'red', 'green']
我试过了
b = a.split(' ')
但它返回一个错误:'numpy.ndarray' object has no attribute 'split'
使用面板用户指南-此页面(https://panel.pyviz.org/user_guide/Components.html)上使用.extend的任何示例似乎都不起作用,正在返回 AttributeError: 'Tabs' object has no attribute 'extend' 然后gridspec示例返回: AttributeError: module 'panel' has no attribute 'GridSpec' 当前使用Panel 0.3.1 本
当使用自定义类的numpy array时:
class TestClass:
active = False
如何使用内联掩码(布尔索引数组),如下所示:
直接尝试失败:
items = np.array([TestClass() for _ in range(10)])
items[items.active]
AttributeError: 'numpy.ndarray' object has no attribute 'active'
有什么建议吗?
在sklearn中实现的随机森林中有apply(X)函数-是否有与GBRT等效的函数?
编辑:
for estimator in gbrt.estimators_:
estimator.tree_.apply(X)
提供:
File "<pyshell#29>", line 2, in <module>
estimator.tree_.apply(Z)
AttributeError: 'numpy.ndarray' object has no attribute 'tree_'
我想向python数组添加一个描述。
例如,当使用numpy作为一种交互式数据语言时,我想做这样的事情:
A = np.array([[1,2,3],[4,5,6]])
A.description = "Holds all the data from experiment 1. Each row contains an intensity measurement with the following columns: time [s], intensity [W/m^2], error [%]."
但它提供了:
Traceback (most recent call last)
当我使用以下命令时:
import pandas as pd
data = pd.read_csv('C:/Users/Z/OneDrive/Python/Exploratory Data/Aramark/ARMK.csv')
x = data.iloc[:,2]
y = pd.unique(x)
y.to_csv('yah.csv')
我得到以下错误:
AttributeError: 'numpy.ndarray' object has no attribute 'to_csv'
我已经使用Xgboost编写了波士顿房价的代码,下面是代码 import treelite
import xgboost
from sklearn.datasets import load_boston
import treelite.runtime # runtime module
X, y = load_boston(return_X_y=True)
print('dimensions of X = {}'.format(X.shape))
print('dimensions of y = {}'.format(y.shape))
dtrain
import scipy as sp
import numpy as np
a=sp.sparse.coo_matrix(np.random.randint(0,9,[4,5]))
b=sp.sparse.coo_matrix(np.random.randint(0,9,[4,2]))
sp.hstack([a,b]).toarray()
就是给我
AttributeError: 'numpy.ndarray' object has no attribute 'toarray'
你能帮我解决我的愚蠢错误吗?
我有一个数组数组,我试图找到其中的最低非零值。
minima = []
for array in K: #where K is my array of arrays (all floats)
if 0.0 in array:
array.remove(0.0)
minima.append(min(array))
print min(minima)
这会产生
AttributeError: 'numpy.ndarray' object has no attribute 'remove'
我认为array.remove()是删除元素
下面是一个场景:
我有以下变量:
val = [('am', '<f8'), ('fr', '<f8')] # val is type numpy.recarray
am = [12.33, 1.22, 5.43, 15.23] # am is type numpy.ndarray
fr = [0.11, 1.23, 2.01, 1.01] # fr is type numpy.ndarray
我需要的是检测am = 12.33和am = 15.23的索引,一旦提取(在本例中索引是[0]和[
当我试图重新平衡有偏见的数据时,我得到了以下属性错误:
'numpy.ndarray' object has no attribute 'value_counts';
似乎y.value_counts()行给出了属性错误。
代码:
X = df.drop(columns=['type', 'quality'])
y = df['quality']
from imblearn.over_sampling import SMOTE
oversample = SMOTE(k_neighbors=5)
X, y =
我试图找到一个预测的Y值(输出是数字的)与x输入使用字符串(例如。业务类型、部门和地区)。使用后:
print(model.predict([['Finance and Control'], ['EMEA'], ['Professional Services']]))
它返回了以下错误:AttributeError: 'numpy.ndarray' object has no attribute 'predict'
import pickle
model = pickle.load(open('model3
我正试着给熊猫读一份拼花卷宗
data=pd.read_parquet('MyFiles.parquet', engine='pyarrow')
但是我得到了以下错误
ArrowInvalid: Casting from timestamp[us] to timestamp[ns] would result in out of bounds timestamp: 253402214400000000
如果我将引擎类型改为紧固件
data=pd.read_parquet('MyFiles.parquet', engine='fastpar
我写了一个这样的代码:
for i in range(60, len(train_data)):
x_train.append(train_data[i-60:i, 0])
但随后它会一直返回一个错误消息
x_train.append(train_data[i-60:i, 0])
AttributeError: 'numpy.ndarray' object has no attribute 'append'"
有没有人能帮我看看里面有什么问题?谢谢
我有一个二维的物体数组。我想迭代这个数组,并打印每个对象的一些属性。下面是我的代码:
import numpy as np
class example:
def __init__(self):
self.number = 1
a = example()
b = example()
c = example()
d = example()
array = np.array([[a,b],[c,d]],dtype=np.object)
for x in np.nditer(array,["refs_ok"]):
print x
错误消息:AttributeError
我写了下面的代码(*)
当我尝试在js控制台中运行以下代码(**)时,
我得到了以下结果:
"your attributes are: ", Object // json object taken from the server as I was expecting
Object function (a){return new n(a)} has no method 'has'
为什么我会遇到有关has no method 'has'的问题
-
(**)
require.config({
baseUrl: "/"
的答案回答了OpenCV 1的问题:您使用图像的Mat.channels()方法。
但是在cv2 (我使用的是2.4.6)中,图像数据结构没有channels()方法。我正在使用Python2.7。
代码片段:
cam = cv2.VideoCapture(source)
ret, img = cam.read()
# Here's where I would like to find the number of channels in img.
互动尝试:
>>> img.channels()
Traceback (most recent call last):