fit (train_dataset,train_label),但是fit(X,y,sample_weight=None)的拟合参数有数组(X).dim< 2和数组(Y).dim<2。但是它需要很大的空间,即尺寸为200x (28 * 28)的2d数组,也可以映射到一维train_label?训练时出错:
ValueError: Foundarray with dim3.Estimator
直观地,我会将数据格式化为n_observations x n_time_points x n_features的三维数组,但是hmmlearn似乎想要一个2d数组。np.random.rand(10,5,3)clf.fit(X)ValueError: Foundarray with dim3.Estimatorexpected <= <e
22/normalised.hdr')som = su.SOMClassifier(n_rows=data.shape[0], n_columns=data.shape[1])ValueError: estimator requires y to be passed, but the target y is Nonesom = su.SOMClustering(n_rows=data.shape[0], n_columns=data.shape[1])
可以使用以下代码生成错误ValueError: Foundarray with dim3.Estimatorexpected <= 2. CCA_model = CCA(n_components= 3, max_iter=20000) input_arr = [[[k*-1+j*-i*-1 for k in range(125)] for j i
我试图解析这个文件来运行多特征回归,但是我得到了一个"ValueError: Found with dim3.Estimator <= 2.“。顺便说一句,输入必须是一个浮点数吗?splitted = line.split() lsty.append(float(splitted[10]))
x = x_.asty