在这个函数中，因子k返回字典a中的值，然后将值保存到列表中，接着通过将fid_t作为scaler进行Fit_transforming返回列表，但是出现了错误。问题出在哪里？
def determineRank2(t, target, bid_t, k): encode = LabelEncoder() scaler = MinMaxScaler() # x = np.concatenate((t,n,bid_t,w,h,k),axis = 1).reshape(1,6,1) t = categorize_time(t) bid_t = scaler.fit_transform(bid_t) a = {'a':0, 'b':1, 'c':2, 'd':3, 'e':4, 'f':5, 'g':6, 'h':7, 'i':8, 'j':9, 'k':10, 'l':11, 'm':12, 'n':13, 'o':14, 'p':15, 'q':16, 'r':17,'w':18, 'x':19, 'y':20, 'z':21, 'ab':22, 'cd':23, 'ef':24, 'gh':25, 'qw':26, 'er':27, 'dz':28, 'df':29} new_list = [] new_list = [t,target,bid_t,k] new_list = np.array(new_list) new_list = new_list.reshape(1, 1, 4) rank = model.predict(new_list) rank = round(rank.item(0)) return rank当我输入类似这样的值时
determineRank2("21:30:04", 3620,2 , "a")错误显示如下
<ipython-input-76-cea3690890e8> in determineRank2(t, target, bid_t, k) 11 # x = np.concatenate((t,n,bid_t,w,h,k),axis = 1).reshape(1,6,1) 12 t = categorize_time(t)---> 13 bid_t = scaler.fit_transform(bid_t)/usr/local/lib/python3.6/dist-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)551 if y is None:552 # fit method of arity 1 (unsupervised transformation)--> 553 return self.fit(X, **fit_params).transform(X)554 else:555 # fit method of arity 2 (supervised transformation)/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py in fit(self, X, y)323 # Reset internal state before fitting324 self._reset()--> 325 return self.partial_fit(X, y)326 327 def partial_fit(self, X, y=None):/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py in partial_fit(self, X, y)351 X = check_array(X, copy=self.copy,352 estimator=self, dtype=FLOAT_DTYPES,--> 353 force_all_finite="allow-nan")354 355 data_min = np.nanmin(X, axis=0)/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)512 "Reshape your data either using array.reshape(-1, 1) if "513 "your data has a single feature or array.reshape(1, -1) "--> 514 "if it contains a single sample.".format(array))515 # If input is 1D raise error516 if array.ndim == 1:ValueError: Expected 2D array, got scalar array instead:array=2.0.Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.我该如何修复它？
回答：
正如错误信息所述，你应该重塑你的 bid_t 值。
从 sklearn 的 scaler 文档 中可以看到：
参数: X: 形状为 [n_samples, n_features] 的 numpy 数组
这里你的 bid_t 甚至不是一个数组。所以你必须让它看起来像一个数组：
determineRank2("21:30:04", 3620, np.array([[2]]) , "a")