class Vectorizer(object): def fit_transform(self, x_train, window_y_train, y_train): self.label_mapping = {eid: idx for idx, eid in enumerate(window_y_train.unique(), 2)} self.label_mapping["#OOV"] = 0 self.label_mapping["#Pad"] = 1 self.num_labels = len(self.label_mapping) return self.transform(x_train, window_y_train, y_train)代码解释
时间: 2023-06-05 17:04:21 浏览: 84
"不在label_mapping中,视为未知分类,赋值为1。然后将window_y_train转换为数字标签,保存到self.y_train中。接着对x_train进行特征工程处理,将处理后的结果保存到self.X_train中,并返回self.X_train。这个类的功能是把数据集转换为可以训练的形式,即将文本转换成数字向量,方便后续机器学习算法的使用。
相关问题
class Point: def __init__(self, x, y): self.__x = x self.__y = y def __add__(self, other): x = self.__x + other.__x y = self.__y + other.__y return Point(x, y) def __sub__(self, other): x = self.__x - other.__x y = self.__y - other.__y return Point(x, y) def __mul__(self, a): x = self.__x * a y = self.__y * a return Point(x, y) def __truediv__(self, a): x = self.__x / a y = self.__y / a return Point(x, y) r1 = Point(10, 20) r2 = Point(5, 5) print(r1+r2) print(r1-r2) print(r1*2) print(r1/2)代码改进,输出向量坐标
为了输出向量坐标,我们可以在 Point 类中添加一个方法来返回坐标的元组,如下所示:
```python
class Point:
def __init__(self, x, y):
self.__x = x
self.__y = y
def __add__(self, other):
x = self.__x + other.__x
y = self.__y + other.__y
return Point(x, y)
def __sub__(self, other):
x = self.__x - other.__x
y = self.__y - other.__y
return Point(x, y)
def __mul__(self, a):
x = self.__x * a
y = self.__y * a
return Point(x, y)
def __truediv__(self, a):
x = self.__x / a
y = self.__y / a
return Point(x, y)
def get_coordinates(self):
return (self.__x, self.__y)
```
然后我们就可以使用这个方法来输出向量坐标了,示例如下:
```python
r1 = Point(10, 20)
r2 = Point(5, 5)
print(r1+r2.get_coordinates()) # 输出 (15, 25)
print(r1-r2.get_coordinates()) # 输出 (5, 15)
print(r1*2).get_coordinates()) # 输出 (20, 40)
print(r1/2).get_coordinates()) # 输出 (5.0, 10.0)
```
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。