importnumpyasnp array_2d=np.array([[1,3,5],[7,5,2]])max_indices=np.argmax(array_2d,axis=1)print(max_indices) Python Copy Output: 示例代码 3: 在列上找到最大值的索引 importnumpyasnp array_2d=np.array([[1,7],[3,5],[5,2]])max_indices=np.argmax(array_2d,axis=0)print(max...
print(np.max(my_array)) # Get max of all array values # 6…and to compute the minimum value, we can apply the min function as illustrated in the following Python code:print(np.min(my_array)) # Get min of all array values # 1...
a = np.array([3, 1, 2, 4, 6, 1]) print(np.argmax(a)) 当没有指定axis的时候,默认是0.所以最后输出的是4(也就是表示第四维值最大) 2.二维数组 import numpy as np a = np.array([[1, 5, 4, 2], [9, 6, 2, 8], [3, 7, 9, 1]]) print(np.argmax(a, axis=0)) 最后...
_sort_ascend(pn_locs, *pn_npks ); } void maxim_sortascendint32_t *pn_x,int32_t n_size) /** * \brief Sort array * \par Details * Sort array in ascending order(insertion sort algorithm) * * \retvalNone */ { int32_t i, j, n_temp; for (i = ; i...
已知x = np.array((1, 2, 3, 4, 5)),那么表达式(x**2).max()的值为___。A.30B.15C.60D.25
npks)++] = pn_locs[j]; } } // Resortindices longo ascending order maxim_sort_ascend( pn_locs, *pn_npks ); } void maxim_sort_ascendint32_t *pn_x,int32_t n_size) /** * \brief Sort array \par Details * Sort array in ascending(insertion sort algorithm) *...
[i] has label c, where 0 <= c < C. - reg: (float) regularization strength Returns a tuple of: - loss as single float - gradient with respect to weights W; an array of same shape as W """# Initialize the loss and gradient to zero.loss=0.0dW=np.zeros_like(W)dW_each=np....
>>> X_train = np.array([[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]]) ... >>> max_abs_scaler = preprocessing.MaxAbsScaler() >>> X_train_maxabs = max_abs_scaler.fit_transform(X_train)
array([1, 1, 1]) 代表了每一列的最大值的位置 >>>np.argmax(a, axis=1) array([2, 2]) 代表了每一行的最大值的位置 与MAX函数之间的区别: y = f(t) 是一般常見的函数式,如果給定一个t值,f(t)函数式会赋一个值給y。 y = max f(t) 代表:y 是f(t)函式所有的值中最大的output。
Y=np.array(Y)returnX, Ydefload_CIFAR10(ROOT):"""load all of cifar"""xs=[] ys=[]#第二步:使用列表数据添加,并使用np.concatenate进行串接,去除矩阵的维度forbinrange(1,2): f= os.path.join(ROOT,'data_batch_%d'%(b, )) X, Y=load_CIFAR_batch(f) ...