included = np.array([[0 for j in np.arange(len(solution.tour))] for i in np.arange(num_customers + 1)])这段代码是什么意思
时间: 2024-05-17 19:16:58 浏览: 18
这段代码创建了一个大小为 `(num_customers+1) x len(solution.tour)` 的二维 numpy 数组 `included`,并将其所有元素初始化为 0。其中,`num_customers` 表示顾客数量,`solution.tour` 是一个包含所有顾客访问顺序的列表。
具体来说,代码中使用了列表推导式和 NumPy 数组创建函数 `np.array()` 来创建二维数组。`np.arange(len(solution.tour))` 返回一个长度为 `len(solution.tour)` 的数组,其中包含了从 0 开始的整数序列。因此,第一维的循环变量 `i` 取值范围为 `np.arange(num_customers + 1)`,第二维的循环变量 `j` 取值范围为 `np.arange(len(solution.tour))`,这样就可以遍历数组的所有元素并初始化为 0。
这个数组的作用是记录每个顾客是否包含在当前解决方案中。具体来说,如果 `included[i][j]` 的值为 1,则表示第 `i` 个顾客包含在当前解决方案的第 `j` 个位置中;如果值为 0,则表示第 `i` 个顾客不包含在当前解决方案中的第 `j` 个位置中。
相关问题
np.linspace
`numpy` function `linspace` returns evenly spaced numbers over a specified interval. The function takes three arguments: `start`, `stop`, and `num`.
Syntax:
```python
numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
```
where,
- `start` : scalar or array_like, optional. The starting value of the sequence.
- `stop` : scalar or array_like. The end value of the sequence, unless `endpoint` is set to `False`.
- `num` : int, optional. Number of samples to generate. Default is `50`.
- `endpoint` : bool, optional. If `True`, the `stop` value is included in the sequence. Default is `True`.
- `retstep` : bool, optional. If `True`, return the step size between consecutive elements. Default is `False`.
- `dtype` : dtype, optional. The type of the output array. Default is `None`.
- `axis` : int, optional. The axis in the result to store the samples. Default is `0`.
Returns:
- `samples` : ndarray. An array of evenly spaced numbers over a specified interval.
Example:
```python
import numpy as np
# Return 5 evenly spaced numbers between -2 and 2
samples = np.linspace(-2, 2, 5)
print(samples)
# Output: [-2. -1. -0. 1. 2.]
```
自变量和因变量都是nd.array类型,给出dw检验和逐步回归的python代码
下面是一个示例代码,其中X和y是自变量和因变量的nd.array类型:
```
import numpy as np
import statsmodels.api as sm
# DW检验
def dw_test(resid):
dw = np.sum(np.diff(resid)**2) / np.sum(resid**2)
return dw
# 逐步回归
def stepwise_regression(X, y):
results = []
p_values = np.ones(X.shape[1])
included = np.zeros(X.shape[1], dtype=bool)
for i in range(X.shape[1]):
best_p = np.inf
best_j = -1
for j in range(X.shape[1]):
if not included[j]:
model = sm.OLS(y, sm.add_constant(X[:, included + (j,)]))
result = model.fit()
if result.pvalues[-1] < best_p:
best_p = result.pvalues[-1]
best_j = j
if best_j >= 0:
model = sm.OLS(y, sm.add_constant(X[:, included + (best_j,)]))
result = model.fit()
included[best_j] = True
p_values[best_j] = best_p
results.append(result)
return results, p_values, included
# 测试代码
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([1, 2, 3])
results, p_values, included = stepwise_regression(X, y)
print('DW检验结果:', dw_test(results[-1].resid))
print('逐步回归结果:', included)
```
请注意,这只是一个示例代码,具体实现可能因数据类型、数据大小等因素而有所不同。
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