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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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scikit-learn/svm/README.md
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@ -66,20 +66,20 @@ a_y: np.ndarray = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis]
data_train_0: np.ndarray = np.concatenate((a_x, a_y), axis=-1) data_train_0: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
class_train_0: np.ndarray = np.full((data_train_0.shape[0],), -1) class_train_0: np.ndarray = np.full((data_train_0.shape[0],), -1)
a_x: np.ndarray = rng.normal(1.5, 1.0, size=(1000))[:, np.newaxis] a_x = rng.normal(1.5, 1.0, size=(1000))[:, np.newaxis]
a_y: np.ndarray = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis] a_y = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis]
data_test_0: np.ndarray = np.concatenate((a_x, a_y), axis=-1) data_test_0: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
class_test_0: np.ndarray = np.full((data_test_0.shape[0],), -1) class_test_0: np.ndarray = np.full((data_test_0.shape[0],), -1)
del a_x del a_x
del a_y del a_y
a_x: np.ndarray = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis] a_x = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
a_y: np.ndarray = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis] a_y = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
data_train_1: np.ndarray = np.concatenate((a_x, a_y), axis=-1) data_train_1: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
class_train_1: np.ndarray = np.full((data_train_0.shape[0],), +1) class_train_1: np.ndarray = np.full((data_train_0.shape[0],), +1)
a_x: np.ndarray = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis] a_x = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
a_y: np.ndarray = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis] a_y = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
data_test_1: np.ndarray = np.concatenate((a_x, a_y), axis=-1) data_test_1: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
class_test_1: np.ndarray = np.full((data_test_0.shape[0],), +1) class_test_1: np.ndarray = np.full((data_test_0.shape[0],), +1)
del a_x del a_x
@ -96,6 +96,8 @@ np.save("label_train.npy", label_train)
np.save("label_test.npy", label_test) np.save("label_test.npy", label_test)
``` ```
## Train and test
```python ```python
``` ```