PY @python_tasks
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from sklearn.cluster import KMeans
import numpy as np
X = np.array([[1, 10], [2, 20], [100, 9000]])
KMeans(n_clusters=2).fit(X)
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
model = LogisticRegression(max_iter=200).fit(X, y)
print(model.predict([X[0]]))
import numpy as np
a = np.array([10, 20, 30, 40])
b = a[1:3]
b[0] = 99
print(a)
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
model = LogisticRegression(max_iter=200).fit(X, y)
print(model.score(X, y))
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
model = LogisticRegression(max_iter=200).fit(X, y)
print(model.predict_proba([X[0]]))
from sklearn.datasets import load_iris; from sklearn.linear_model import LogisticRegression
print((lambda X, y: LogisticRegression(max_iter=200).fit(X, y).score(X, y))(*load_iris(return_X_y=True)))
Forwarded from Alex P
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
model = Pipeline([
("scale", StandardScaler()),
("clf", LogisticRegression())
])
Forwarded from Alex P
def search(d, target):
for k, v in d.items():
if k == target:
return v
if isinstance(v, dict):
result = search(v, target)
if result is not None:
return result
return None
data = {'a': {'b': {'c': 42}}, 'x': 1}
print(search(data, 'c'))