Compute the q-th quantile(s) of the data along the specified axis.
Arguments
- x
Input tensor.
- q
Probability or sequence of probabilities for the quantiles to compute. Values must be between 0 and 1 inclusive.
- axis
Axis or axes along which the quantiles are computed. Defaults to
axis=NULLwhich is to compute the quantile(s) along a flattened version of the array.- method
A string specifies the method to use for estimating the quantile. Available methods are
"linear","lower","higher","midpoint", and"nearest". Defaults to"linear". If the desired quantile lies between two data pointsi < j:"linear":i + (j - i) * fraction, where fraction is the fractional part of the index surrounded byiandj."lower":i."higher":j."midpoint":(i + j) / 2"nearest":iorj, whichever is nearest.
- keepdims
If this is set to
TRUE, the axes which are reduce are left in the result as dimensions with size one.
Value
The quantile(s). If q is a single probability and axis=NULL, then
the result is a scalar. If multiple probabilies levels are given, first
axis of the result corresponds to the quantiles. The other axes are the
axes that remain after the reduction of x.
See also
Other numpy ops: op_abs() op_add() op_all() op_any() op_append() op_arange() op_arccos() op_arccosh() op_arcsin() op_arcsinh() op_arctan() op_arctan2() op_arctanh() op_argmax() op_argmin() op_argpartition() op_argsort() op_array() op_average() op_bincount() op_broadcast_to() op_ceil() op_clip() op_concatenate() op_conj() op_copy() op_correlate() op_cos() op_cosh() op_count_nonzero() op_cross() op_ctc_decode() op_cumprod() op_cumsum() op_diag() op_diagonal() op_diff() op_digitize() op_divide() op_divide_no_nan() op_dot() op_einsum() op_empty() op_equal() op_exp() op_expand_dims() op_expm1() op_eye() op_flip() op_floor() op_floor_divide() op_full() op_full_like() op_get_item() op_greater() op_greater_equal() op_hstack() op_identity() op_imag() op_isclose() op_isfinite() op_isinf() op_isnan() op_less() op_less_equal() op_linspace() op_log() op_log10() op_log1p() op_log2() op_logaddexp() op_logical_and() op_logical_not() op_logical_or() op_logical_xor() op_logspace() op_lstsq() op_matmul() op_max() op_maximum() op_mean() op_median() op_meshgrid() op_min() op_minimum() op_mod() op_moveaxis() op_multiply() op_nan_to_num() op_ndim() op_negative() op_nonzero() op_not_equal() op_ones() op_ones_like() op_outer() op_pad() op_power() op_prod() op_ravel() op_real() op_reciprocal() op_repeat() op_reshape() op_roll() op_round() op_select() op_sign() op_sin() op_sinh() op_size() op_sort() op_split() op_sqrt() op_square() op_squeeze() op_stack() op_std() op_subtract() op_sum() op_swapaxes() op_take() op_take_along_axis() op_tan() op_tanh() op_tensordot() op_tile() op_trace() op_transpose() op_tri() op_tril() op_triu() op_var() op_vdot() op_vectorize() op_vstack() op_where() op_zeros() op_zeros_like()
Other ops: op_abs() op_add() op_all() op_any() op_append() op_arange() op_arccos() op_arccosh() op_arcsin() op_arcsinh() op_arctan() op_arctan2() op_arctanh() op_argmax() op_argmin() op_argpartition() op_argsort() op_array() op_associative_scan() op_average() op_average_pool() op_batch_normalization() op_binary_crossentropy() op_bincount() op_broadcast_to() op_cast() op_categorical_crossentropy() op_ceil() op_cholesky() op_clip() op_concatenate() op_cond() op_conj() op_conv() op_conv_transpose() op_convert_to_numpy() op_convert_to_tensor() op_copy() op_correlate() op_cos() op_cosh() op_count_nonzero() op_cross() op_ctc_decode() op_ctc_loss() op_cumprod() op_cumsum() op_custom_gradient() op_depthwise_conv() op_det() op_diag() op_diagonal() op_diff() op_digitize() op_divide() op_divide_no_nan() op_dot() op_dtype() op_eig() op_eigh() op_einsum() op_elu() op_empty() op_equal() op_erf() op_erfinv() op_exp() op_expand_dims() op_expm1() op_extract_sequences() op_eye() op_fft() op_fft2() op_flip() op_floor() op_floor_divide() op_fori_loop() op_full() op_full_like() op_gelu() op_get_item() op_greater() op_greater_equal() op_hard_sigmoid() op_hard_silu() op_hstack() op_identity() op_imag() op_image_affine_transform() op_image_crop() op_image_extract_patches() op_image_hsv_to_rgb() op_image_map_coordinates() op_image_pad() op_image_resize() op_image_rgb_to_grayscale() op_image_rgb_to_hsv() op_in_top_k() op_inv() op_irfft() op_is_tensor() op_isclose() op_isfinite() op_isinf() op_isnan() op_istft() op_leaky_relu() op_less() op_less_equal() op_linspace() op_log() op_log10() op_log1p() op_log2() op_log_sigmoid() op_log_softmax() op_logaddexp() op_logical_and() op_logical_not() op_logical_or() op_logical_xor() op_logspace() op_logsumexp() op_lstsq() op_lu_factor() op_map() op_matmul() op_max() op_max_pool() op_maximum() op_mean() op_median() op_meshgrid() op_min() op_minimum() op_mod() op_moments() op_moveaxis() op_multi_hot() op_multiply() op_nan_to_num() op_ndim() op_negative() op_nonzero() op_norm() op_normalize() op_not_equal() op_one_hot() op_ones() op_ones_like() op_outer() op_pad() op_power() op_prod() op_psnr() op_qr() op_ravel() op_real() op_reciprocal() op_relu() op_relu6() op_repeat() op_reshape() op_rfft() op_roll() op_round() op_rsqrt() op_scan() op_scatter() op_scatter_update() op_searchsorted() op_segment_max() op_segment_sum() op_select() op_selu() op_separable_conv() op_shape() op_sigmoid() op_sign() op_silu() op_sin() op_sinh() op_size() op_slice() op_slice_update() op_slogdet() op_softmax() op_softplus() op_softsign() op_solve() op_solve_triangular() op_sort() op_sparse_categorical_crossentropy() op_split() op_sqrt() op_square() op_squeeze() op_stack() op_std() op_stft() op_stop_gradient() op_subtract() op_sum() op_svd() op_swapaxes() op_switch() op_take() op_take_along_axis() op_tan() op_tanh() op_tensordot() op_tile() op_top_k() op_trace() op_transpose() op_tri() op_tril() op_triu() op_unstack() op_var() op_vdot() op_vectorize() op_vectorized_map() op_vstack() op_where() op_while_loop() op_zeros() op_zeros_like()