<ruby id="bdb3f"></ruby>

    <p id="bdb3f"><cite id="bdb3f"></cite></p>

      <p id="bdb3f"><cite id="bdb3f"><th id="bdb3f"></th></cite></p><p id="bdb3f"></p>
        <p id="bdb3f"><cite id="bdb3f"></cite></p>

          <pre id="bdb3f"></pre>
          <pre id="bdb3f"><del id="bdb3f"><thead id="bdb3f"></thead></del></pre>

          <ruby id="bdb3f"><mark id="bdb3f"></mark></ruby><ruby id="bdb3f"></ruby>
          <pre id="bdb3f"><pre id="bdb3f"><mark id="bdb3f"></mark></pre></pre><output id="bdb3f"></output><p id="bdb3f"></p><p id="bdb3f"></p>

          <pre id="bdb3f"><del id="bdb3f"><progress id="bdb3f"></progress></del></pre>

                <ruby id="bdb3f"></ruby>

                ??一站式輕松地調用各大LLM模型接口,支持GPT4、智譜、豆包、星火、月之暗面及文生圖、文生視頻 廣告
                [TOC] # 求和 ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) print(tang_array) # 求和 npSum = np.sum(tang_array) print(np.sum(tang_array)) ~~~ 輸出 ~~~ [[1 2 3] [4 5 6]] 21 ~~~ # 指定要沿著什么軸(維度)求和 ## 豎著求和 ![](https://box.kancloud.cn/86e180d055b53ae79b79866095aa1dd4_214x122.png) ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 也可以寫成tang_array.sum(axis=0) npSum = np.sum(tang_array, axis=0) print(npSum) ~~~ 輸出 `[5 7 9]` ## 橫著求和 ![](https://box.kancloud.cn/eae9fe4d0b9f83cb3172b14185250fb4_230x116.png) ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 也可以寫成tang_array.sum(axis=1) # axis = -1也可以 npSum = np.sum(tang_array, axis=1) print(npSum) ~~~ 輸出 `[ 6 15]` # 乘積 ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 豎著乘 rel = tang_array.prod(axis=0) print(rel) # 橫著乘,都乘起來不寫參數 result = tang_array.prod(axis=1) print(result) ~~~ 輸出 ~~~ [ 4 10 18] [ 6 120] ~~~ # 取最小值 ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 全局最小值 rel = tang_array.min() print(rel) # 豎著取最小值 Vertically = tang_array.min(axis=0) print(Vertically) # 橫著最小值 Sideways = tang_array.min(axis=1) print(Sideways) ~~~ 輸出 ~~~ 1 [1 2 3] [1 4] ~~~ 相應最大值是max **求最小值的索引** ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 如果要那個維度最小的索引就加參數axis= argmin = tang_array.argmin() print(argmin) ~~~ 輸出 `0` # 求均值 ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 全局的均值 mean = tang_array.mean() print(mean) # 求豎著的均值 array_mean = tang_array.mean(axis=0) print(array_mean) ~~~ 輸出 ~~~ 3.5 [ 2.5 3.5 4.5] ~~~ # 求標準差 ![](https://box.kancloud.cn/67ab480a69e165e7ac88522fda3e4d3f_1226x298.png) ~~~ tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 標準差 std = tang_array.std() print(std) ~~~ 輸出 `1.70782512766` # 求方差 ~~~ # 方差 var = tang_array.var() print(var) ~~~ 輸出 `2.91666666667` # 輸出限制 ~~~ import numpy as np tang_array = np.array([[1, 2, 3], [4, 5, 6]]) # 小于2的值變為2,大于4的值變為4 clip = tang_array.clip(2, 4) print(clip) ~~~ 輸出 ~~~ [[2 2 3] [4 4 4]] ~~~ # 四舍五入 ~~~ import numpy as np tang_array = np.array([[1, 2, 3.1, 4.6, 6.1]]) # 四舍五入 array_round = tang_array.round() print(array_round) ~~~ 輸出 ~~~ [[ 1. 2. 3. 5. 6.]] ~~~ 設置保留精度 ~~~ import numpy as np tang_array = np.array([[1.23, 2.67, 3.12, 4.6, 6.1]]) array_round = tang_array.round(decimals=1) print(array_round) ~~~ 輸出 ~~~ [[ 1.2 2.7 3.1 4.6 6.1]] ~~~
                  <ruby id="bdb3f"></ruby>

                  <p id="bdb3f"><cite id="bdb3f"></cite></p>

                    <p id="bdb3f"><cite id="bdb3f"><th id="bdb3f"></th></cite></p><p id="bdb3f"></p>
                      <p id="bdb3f"><cite id="bdb3f"></cite></p>

                        <pre id="bdb3f"></pre>
                        <pre id="bdb3f"><del id="bdb3f"><thead id="bdb3f"></thead></del></pre>

                        <ruby id="bdb3f"><mark id="bdb3f"></mark></ruby><ruby id="bdb3f"></ruby>
                        <pre id="bdb3f"><pre id="bdb3f"><mark id="bdb3f"></mark></pre></pre><output id="bdb3f"></output><p id="bdb3f"></p><p id="bdb3f"></p>

                        <pre id="bdb3f"><del id="bdb3f"><progress id="bdb3f"></progress></del></pre>

                              <ruby id="bdb3f"></ruby>

                              哎呀哎呀视频在线观看