La función make_moons — 3:40 min

• 3:40 min | Ultima modificación: Septiembre 27, 2021 | YouTube

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons

Esta función permite la creación de dos semicírculos concentricos con aspecto de luna, donde cada semicírculo representa una clase.

import matplotlib.pyplot as plt
from sklearn.datasets import make_moons

X, y = make_moons(
    # -------------------------------------------------------------------------
    # If int, the total number of points generated. If two-element tuple,
    # number of points in each of two moons.
    n_samples=100,
    # -------------------------------------------------------------------------
    # Shuffle the samples.
    shuffle=False,
    # -------------------------------------------------------------------------
    # Standard deviation of Gaussian noise added to the data.
    noise=0.0,
    # -------------------------------------------------------------------------
    # Determines random number generation for dataset shuffling and noise. Pass
    # an int for reproducible output across multiple function calls.
    # creation.
    random_state=12346,
)


plt.figure(figsize=(7, 7))
plt.scatter(
    X[y == 0, 0],
    X[y == 0, 1],
    color="tab:red",
    edgecolors="k",
    s=120,
    alpha=0.9,
)
plt.scatter(
    X[y == 1, 0],
    X[y == 1, 1],
    color="tab:blue",
    edgecolors="k",
    s=120,
    alpha=0.9,
)


plt.gca().spines["left"].set_color("gray")
plt.gca().spines["bottom"].set_color("gray")
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.axis("equal")
plt.show()

X, y = make_moons(
    n_samples=100,
    shuffle=False,
    noise=0.1,
    # -------------------------------------------------------------------------
    # Determines random number generation for dataset shuffling and noise. Pass
    # an int for reproducible output across multiple function calls.
    # creation.
    random_state=12346,
)


plt.figure(figsize=(7, 7))
plt.scatter(
    X[y == 0, 0],
    X[y == 0, 1],
    color="tab:red",
    edgecolors="k",
    s=120,
    alpha=0.9,
)
plt.scatter(
    X[y == 1, 0],
    X[y == 1, 1],
    color="tab:blue",
    edgecolors="k",
    s=120,
    alpha=0.9,
)


plt.gca().spines["left"].set_color("gray")
plt.gca().spines["bottom"].set_color("gray")
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.axis("equal")
plt.show()