window_size = 50 y = np.zeros(len(x)-window_size+1) for i in range(len(y)): y[i] = np.mean(x[i:i+window_size])
时间: 2023-05-10 13:56:07 浏览: 48
这段代码的作用是什么?
这段代码的作用是计算一个长度为 window_size 的滑动窗口内的数据的平均值,并将这些平均值存储在 y 数组中。其中,x 是原始数据,y 的长度为 len(x) - window_size + 1。
相关问题
class TimeWindowSequence(Sequence): def __init__(self, x, y, batch_size, window_size): self.x = x self.y = y self.batch_size = batch_size self.window_size = window_size self.window_count = int(np.ceil(x.shape[0] / window_size)) def __len__(self): return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, idx): batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1])) batch_y = np.zeros((self.batch_size, self.y.shape[1])) for i in range(self.batch_size): j = idx * self.batch_size + i if j >= self.window_count: break window_x = self.x[j*self.window_size:(j+1)*self.window_size, :] window_y = self.y[j*self.window_size:(j+1)*self.window_size, :] batch_x[i, :window_x.shape[0], :] = window_x batch_y[i, :] = window_y[-1, :] return batch_x, batch_y出现
这段代码中似乎缺少了一个缩进,导致 `return` 语句在 `for` 循环内部。可以尝试将 `return` 语句缩进到 `for` 循环外部,如下所示:
```
class TimeWindowSequence(Sequence):
def __init__(self, x, y, batch_size, window_size):
self.x = x
self.y = y
self.batch_size = batch_size
self.window_size = window_size
self.window_count = int(np.ceil(x.shape[0] / window_size))
def __len__(self):
return int(np.ceil(self.x.shape[0] / self.batch_size))
def __getitem__(self, idx):
batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1]))
batch_y = np.zeros((self.batch_size, self.y.shape[1]))
for i in range(self.batch_size):
j = idx * self.batch_size + i
if j >= self.window_count:
break
window_x = self.x[j*self.window_size:(j+1)*self.window_size, :]
window_y = self.y[j*self.window_size:(j+1)*self.window_size, :]
batch_x[i, :window_x.shape[0], :] = window_x
batch_y[i, :] = window_y[-1, :]
return batch_x, batch_y
```
这样应该就可以解决这个问题了。
import numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 5 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)怎么修改不影响首尾值
如果你想修改代码,使得平滑后的结果不影响首尾值,你可以在循环中对边界值进行特殊处理。以下是修改后的代码示例:
```python
import numpy as np
def rts_smooth(data, window_size, smooth_factor):
assert window_size % 2 == 1, "Window size must be odd"
assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1"
half_window = (window_size - 1) // 2
data_length = len(data)
smoothed_data = np.zeros(data_length)
for i in range(half_window, data_length - half_window):
window = data[i - half_window:i + half_window + 1]
median = np.median(window)
deviation = np.abs(window - median)
threshold = smooth_factor * np.median(deviation)
if np.abs(data[i] - median) > threshold:
smoothed_data[i] = median
else:
smoothed_data[i] = data[i]
# 处理首尾值
smoothed_data[:half_window] = data[:half_window]
smoothed_data[data_length - half_window:] = data[data_length - half_window:]
return smoothed_data
# 使用示例
data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23]
window_size = 5
smooth_factor = 0.6
smoothed_data = rts_smooth(data, window_size, smooth_factor)
print(smoothed_data)
```
在这个示例中,我们在循环结束后,对首尾的值进行了处理,将原始的数据的首尾值赋给平滑后的结果。这样就能保持首尾值不受平滑处理的影响。
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