缺失x和y参数的情况下,则输出满足条件(非0)元素的坐标,等价于np.asarray(condition).nonzero()。 # 广播机制 broadcasta = np.array([2,4,6,8,10]) np.where(a >5)# (array([2, 3, 4], dtype=int64),)a[np.where(a >5)]# array([ 6, 8, 10]) 多维数组 a = np.arange(27).reshape...
# 4.如何获取数组a = np.array([1,2,3,2,3,4,3,4,5,6])和数组b = np.array([7,2,10,2,7,4,9,4,9,8])之间的共元素?...# 法1 a = np.array([1,2,3,2,3,4,3,4,5,6]) b = np.array([7,2,10,2,7...
array([0,1,8,19,16,18,10,11,2,13,14,3])# Divide by2andcheckifremainderis1 cond = np.mod(array,2)==1 cond array([False,True,False,True,False,False,False,True,False,True,False,True])# Use extracttogetthe values np.extract(cond,array) array([1,19,11,13,3])# Apply condition...
(8.0) >>> np.split(x, [3, 5, 6, 10]) [array([ 0., 1., 2.]), array([ 3., 4.]), array([ 5.]), array([ 6., 7.]), array([], dtype=float64)] m = np.arange(8.0) n = np.split(m, (3,)) print(n) 结果:[array([0., 1., 2.]), array([3., 4., 5....
可以开始预测') def train_2(self, _y): # 先把数据转化为矩阵,便于接下来切片统计运算 print(_y) data = np.array(_y[1]) # print(_y[1]) # 计算P(Xi=k | Y = 中风与否)的先验概率,统计每个特征的值的种类 count_x1 = Counter(data[:, 0]) count_x2 = Counter(data[:, 1]) count_...
import numpy as np arr = np.array([1, 2, 3, 4, 5]) condition = arr > 2 new_arr = np.where(condition, arr * 2, arr) print(new_arr) # 输出: [1 2 6 8 10] Rs 数据表包 基础概念: Rs 数据表包通常指的是 R 语言中的数据表处理包,如 dplyr 或data.table。 优势: 高效的...
DynamicArray DocumentFormat.OpenXml.Office2019.Excel.PivotDefaultLayout DocumentFormat.OpenXml.Office2019.Excel.RichData DocumentFormat.OpenXml.Office2019.Excel.RichData2 DocumentFormat.OpenXml.Office2019.Excel.ThreadedComments DocumentFormat.OpenXml.Office2019.Presentation DocumentFormat.OpenXml.Office2019.Word.Cid...
AcceptDeclineCustomize Skip navigation links Overview Package Class Use Tree Deprecated Index Help Summary: Nested | Field | Constr | Method Detail: Field | Constr | Method SEARCH Package software.amazon.awscdk.services.mediaconvert Class CfnPreset java.lang.Object software.amazon.jsii.JsiiObject sof...
\begin{aligned} \forall i \in [n]: \; \quad x_i = {\left\{ \begin{array}{ll} 1 &{} \text {if } r_i \ne \bot \text { and }{\mathsf {Com}}(i, r_i) = {\mathsf {c}}_i,\\ 0 &{} \text {otherwise.} \end{array}\right. } \end{aligned} ...
# numpy.unique()实例化 x = np.unique([1,1,2,2,3,3]) print(x) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]]) y,indices = np.unique(x,axis=0,return_index=True) # 返回x的唯一元素+新数组元素在老数组中的索引 print(indices) # 返回老数组元素在新数组元素中的索引...