[英]How do I calculate y for every value of x if the slope a set to 10 and the intercept b to 0?
I have a small set of data .我有一小部分数据。 I used python3 to read it and created a scatter plot .
我使用 python3 读取它并创建了一个散点图 plot 。 My next task is to set the slope a to 10 and the intercept b to 0 and calculate y for every value of x .
我的下一个任务是将斜率a设置为 10,将截距b设置为 0,并为x的每个值计算y 。 The task says I should not use any existing linear regression functions.
该任务说我不应该使用任何现有的线性回归函数。 I have been stuck for some time on this.
我已经被困了一段时间了。 How can I do that?
我怎样才能做到这一点?
If your slope is already set to 10, I don't see why you need to use Linear Regression.如果您的斜率已经设置为 10,我不明白为什么需要使用线性回归。 I hope I'm not missing anything from your task.
我希望我不会从你的任务中遗漏任何东西。
However, keeping that aside if you need to get a list in python with all elements multiplied by your slope a
then you can use a list comprehension to find this new list in the following way:但是,如果您需要在 python 中获取所有元素乘以斜率
a
的列表,则保留这一点,那么您可以使用列表推导通过以下方式找到这个新列表:
y_computed = [item*a for item in x]
You can literally just draw a line with a constant slope (10) on the same plot, then calculate the the predicted y-value based on that line "estimate" (you can also find the error of the estimate if you want).您实际上可以在同一 plot 上绘制一条具有恒定斜率 (10) 的线,然后根据该线“估计”计算预测的 y 值(如果需要,您还可以找到估计的误差)。 That be done using the following:
这可以使用以下方法完成:
import numpy as np
from matplotlib import pyplot as plt
def const_line(x):
y = 10 * x + 0 # Just to illustrate that the intercept is zero
return y
x = np.linspace(0, 1)
y = const_line(x)
plt.plot(x, y, c='m')
plt.show()
# Find the y-values for each sample point in your data:
for x in data:
const_line(x)
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