print("数组维度", array01.ndim) # 数组维度 1 print("矩阵形状", array01.shape) # 矩阵形状 (5,) 一行五列 print("矩阵数据类型", array01.dtype) # float64 data02 = [[1, 2, 3, 4], [5, 6, 7, 8]] array02 = np.array(data02) print("样本矩阵\n", array02) # 样本矩阵 # ...
print('Whole Array:\n', array1)print('Array using Column 0 and 2:\n', array2) Run Code Output Whole Array: [[1. 2. 3.] [4. 5. 6.] [7. 8. 9.]] Array using Column 0 and 2: [[1. 3.] [4. 6.] [7. 9.]] Example 8: Use unpack Argument Theunpackargument is a bo...
| Reduces `array`'s dimension by one, by applying ufunc along one axis. | | Let :math:`array.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then | :math:`ufunc.reduce(array, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` = | the result of iterating `...
print("Array 2, multiplied by two \n", arr2) sum=arr1+arr2 print("Sum of both arrays \n",sum) Array 1, plus one [2 3 4] Array 2, multiplied by two [[ 2 4 6] [ 8 10 12]] Sum of both arrays [[ 4 7 10] [10 13 16]] Numpy Math Functions Numpy has a whole host ...
a1=np.array([1,2,3],dtype=np.float64)print(a1.dtype)a2=np.array([1,2,3],dtype=np.int32)print(a2.dtype)# float64# int32 2)astyoe数据类型的转化 a2=a2.astype(np.float64)print(a2.dtype)print(a2)'''float64[1. 2. 3.]''' ...
print(rand_array.dtype) # array的data type print(rand_array.ndim) # 返回数组的维数,也就是行数 # 把列表转换为矩阵 a = [1, 2, 3, 4] print(a) array = np.array(a, dtype=np.int32) array = array.astype(np.float64) # 把array的类型从int32转换为float64,转换类型会生成一个copy,哪...
print(data.shape) # (2, 3) # 查看数组的类型 print(data.dtype) # float64 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 1.2 array数组 AI检测代码解析 import numpy as np data=[[1,2,3,4],[5,6,7,8]] ...
array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe']) data = np.random.randn(7, 4) print(names) print(data) ['Bob' 'Joe' 'Will' 'Bob' 'Will' 'Joe' 'Joe'] [[-1.51858468 -0.15399436 0.19630062 0.27871254] [ 2.10012124 -0.28707332 -0.88352419 0.78329726] [ 0.35718626 ...
array([[0, 0]]) # shape=(1, 2) hidden = self.sigmoid(np.dot(X, self.w0) + np.dot(np.zeros(self.HIDDEN_DIM), self.wh)) output = self.sigmoid(np.dot(hidden, self.w1)) result = np.round(output[0][0]) print("预测结果为",result) def show(self,): '''显示训练图像''' ...
print(not t) # Logical NOT; prints "False" print(t != f) # Logical XOR; prints "True" 1. 2. 3. 4. 5. 6. 7. 4.Strings(字符串类型) hello = 'hello' # String literals can use single quotes world = "world" # or double quotes; it does not matter. ...