def __len__(self): return len(self.Xs)
时间: 2023-07-30 13:10:35 浏览: 50
这段代码是一个特殊方法,用于获取对象的长度。在这个方法中,self.Xs表示对象中的一个属性,它的长度即为对象的长度。当我们使用len()函数来获取对象长度时,实际上就是调用了这个特殊方法。比如,如果我们有一个列表对象a,那么len(a)就会返回a中元素的个数。在这个方法中,我们可以自定义返回的长度,从而对对象的长度进行控制。
相关问题
def create_dataset(X, y, time_steps=1): Xs, ys = [], [] for i in range(len(X) - time_steps): Xs.append(X.iloc[i:(i + time_steps)].values) ys.append(y.iloc[i + time_steps]) return np.array(Xs), np.array(ys) TIME_STEPS = 10 X_train, y_train = create_dataset(train[['本车速度', '车头间距', '原车道前车速度']], train['本车加速度'], time_steps=TIME_STEPS) X_test, y_test = create_dataset(test[['本车速度', '车头间距', '原车道前车速度']], test['本车加速度'], time_steps=TIME_STEPS) train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) y_train = scaler.inverse_transform([y_train]) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test])报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 49, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)
这个错误通常会在使用sklearn的MinMaxScaler时出现。它表示输出操作数的形状不可广播到给定的形状。
根据你提供的代码,这个错误可能是由于y_train被转换为形状为(1, n_samples)的数组所致。你可以尝试使用ravel()函数将y_train的形状转换为(1, n_samples)的数组。
例如,将以下代码:
```
y_train = scaler.inverse_transform([y_train])
```
替换为:
```
y_train = scaler.inverse_transform(y_train.reshape(-1, 1)).ravel()
```
这应该可以解决这个问题。
如果你需要更具体的解决方案,请提供更多的上下文和代码。
python svm算法smo cifar_使用smo算法编写svm对CIFAR-10数据分类
SVM算法通过将数据映射到高维空间,将数据分为两个类别。SVM算法的目标是找到一个超平面,可以将数据分为两个类别。SMO算法是一种优化算法,用于求解SVM中的二次规划问题。下面介绍如何使用SMO算法编写SVM对CIFAR-10数据进行分类。
首先,我们需要加载CIFAR-10数据集。CIFAR-10数据集包含10个类别的60000个32x32彩色图像。每个类别包含6000个图像。我们将使用Python中的pickle模块来加载数据集。以下是加载数据集的代码:
```python
import pickle
import numpy as np
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
def load_cifar10_data():
xs = []
ys = []
for j in range(5):
d = unpickle('cifar-10-batches-py/data_batch_%d' % (j + 1))
x = d[b'data']
y = d[b'labels']
xs.append(x)
ys.append(y)
d = unpickle('cifar-10-batches-py/test_batch')
xs.append(d[b'data'])
ys.append(d[b'labels'])
x = np.concatenate(xs) / np.float32(255)
y = np.concatenate(ys)
return x.reshape((len(x), -1)), np.array(y)
```
接下来,我们将使用SMO算法来训练SVM模型。以下是使用SMO算法训练SVM模型的代码:
```python
class SVM:
def __init__(self, C, toler, kernel_opt=('linear', 0)):
self.C = C
self.toler = toler
self.kernel_opt = kernel_opt
def fit(self, X, y):
n_samples, n_features = X.shape
alpha = np.zeros(n_samples)
b = 0
kernel = kernel_set[self.kernel_opt[0]]
K = np.zeros((n_samples, n_samples))
for i in range(n_samples):
K[:, i] = kernel(X, X[i], self.kernel_opt[1])
iter = 0
while iter < max_iter:
num_changed_alphas = 0
for i in range(n_samples):
Ei = np.dot(alpha * y, K[:, i]) + b - y[i]
if (y[i] * Ei < -self.toler and alpha[i] < self.C) or \
(y[i] * Ei > self.toler and alpha[i] > 0):
j = np.random.choice([x for x in range(n_samples) if x != i])
Ej = np.dot(alpha * y, K[:, j]) + b - y[j]
alpha_i_old, alpha_j_old = alpha[i], alpha[j]
if y[i] != y[j]:
L = max(0, alpha[j] - alpha[i])
H = min(self.C, self.C + alpha[j] - alpha[i])
else:
L = max(0, alpha[i] + alpha[j] - self.C)
H = min(self.C, alpha[i] + alpha[j])
if L == H:
continue
eta = 2.0 * K[i, j] - K[i, i] - K[j, j]
if eta >= 0:
continue
alpha[j] -= y[j] * (Ei - Ej) / eta
alpha[j] = min(alpha[j], H)
alpha[j] = max(alpha[j], L)
if abs(alpha[j] - alpha_j_old) < 1e-5:
continue
alpha[i] += y[i] * y[j] * (alpha_j_old - alpha[j])
b1 = b - Ei - y[i] * (alpha[i] - alpha_i_old) * K[i, i] - \
y[j] * (alpha[j] - alpha_j_old) * K[i, j]
b2 = b - Ej - y[i] * (alpha[i] - alpha_i_old) * K[i, j] - \
y[j] * (alpha[j] - alpha_j_old) * K[j, j]
if 0 < alpha[i] < self.C:
b = b1
elif 0 < alpha[j] < self.C:
b = b2
else:
b = (b1 + b2) / 2
num_changed_alphas += 1
if num_changed_alphas == 0:
iter += 1
else:
iter = 0
self.X = X
self.y = y
self.kernel = kernel
self.alpha = alpha
self.b = b
def predict(self, X):
n_samples, n_features = X.shape
K = np.zeros((n_samples, len(self.X)))
for i in range(n_samples):
K[i, :] = self.kernel(self.X, X[i], self.kernel_opt[1])
y_pred = np.dot(self.alpha * self.y, K) + self.b
return np.sign(y_pred)
```
最后,我们使用以下代码来加载数据集并使用SMO算法训练SVM模型:
```python
X, y = load_cifar10_data()
y[y == 0] = -1
X_train, X_test = X[:50000], X[50000:]
y_train, y_test = y[:50000], y[50000:]
svm = SVM(C=1.0, toler=0.001, kernel_opt=('rbf', 1))
svm.fit(X_train, y_train)
y_pred_train = svm.predict(X_train)
y_pred_test = svm.predict(X_test)
train_acc = np.mean(y_train == y_pred_train)
test_acc = np.mean(y_test == y_pred_test)
print('train_acc:', train_acc)
print('test_acc:', test_acc)
```
这样我们就使用SMO算法编写了SVM对CIFAR-10数据进行分类的代码。