使用回归变换的多项式拟合图

此示例展示了如何使用回归变换将数据与多个拟合多项式叠加在一起。

方法语法

import numpy as np
import pandas as pd
import altair as alt

# Generate some random data
rng = np.random.RandomState(1)
x = rng.rand(40) ** 2
y = 10 - 1.0 / (x + 0.1) + rng.randn(40)
source = pd.DataFrame({"x": x, "y": y})

# Define the degree of the polynomial fits
degree_list = [1, 3, 5]

base = alt.Chart(source).mark_circle(color="black").encode(
    alt.X("x"),
    alt.Y("y")
)

polynomial_fit = [
    base.transform_regression(
        "x", "y", method="poly", order=order, as_=["x", str(order)]
    )
    .mark_line()
    .transform_fold([str(order)], as_=["degree", "y"])
    .encode(alt.Color("degree:N"))
    for order in degree_list
]

alt.layer(base, *polynomial_fit)

属性语法

import numpy as np
import pandas as pd
import altair as alt

# Generate some random data
rng = np.random.RandomState(1)
x = rng.rand(40) ** 2
y = 10 - 1.0 / (x + 0.1) + rng.randn(40)
source = pd.DataFrame({"x": x, "y": y})

# Define the degree of the polynomial fits
degree_list = [1, 3, 5]

base = alt.Chart(source).mark_circle(color="black").encode(
        alt.X("x"), alt.Y("y")
)

polynomial_fit = [
    base.transform_regression(
        "x", "y", method="poly", order=order, as_=["x", str(order)]
    )
    .mark_line()
    .transform_fold([str(order)], as_=["degree", "y"])
    .encode(alt.Color("degree:N"))
    for order in degree_list
]

alt.layer(base, *polynomial_fit)