有人可以帮我在graalvm pls中将一些python代码转换为R代码吗？

Question

我正在研究一个任务，通过在graalvm中使用python和R来编写梯度下降算法。我实际上是graalvm和R的新手。我拥有python版本的代码，我需要有人帮助我转换R中的'genData'函数。任何帮助将不胜感激。 这是python中的梯度下降代码：

import numpy as np
import random

# m denotes the number of examples here, not the number of features
def gradientDescent(x, y, theta, alpha, m, numIterations):
    xTrans = x.transpose()
    for i in range(0, numIterations):
        hypothesis = np.dot(x, theta)
        loss = hypothesis - y
        # avg cost per example (the 2 in 2*m doesn't really matter here.
        # But to be consistent with the gradient, I include it)
        cost = np.sum(loss ** 2) / (2 * m)
        print("Iteration %d | Cost: %f" % (i, cost))
        # avg gradient per example
        gradient = np.dot(xTrans, loss) / m
        # update
        theta = theta - alpha * gradient
    return theta


def genData(numPoints, bias, variance):
    x = np.zeros(shape=(numPoints, 2))
    y = np.zeros(shape=numPoints)
    # basically a straight line
    for i in range(0, numPoints):
        # bias feature
        x[i][0] = 1
        x[i][1] = i
        # our target variable
        y[i] = (i + bias) + random.uniform(0, 1) * variance
    return x, y

# gen 100 points with a bias of 25 and 10 variance as a bit of noise
x, y = genData(100, 25, 10)
m, n = np.shape(x)
numIterations= 100000
alpha = 0.0005
theta = np.ones(n)
theta = gradientDescent(x, y, theta, alpha, m, numIterations)
print(theta)

这是我尝试用graalvm中的R编写'genData'函数的代码，但是出了点问题。

import numpy as np
import polyglot

# m denotes the number of examples here, not the number of features
def gradientDescent(x, y, theta, alpha, m, numIterations):
    xTrans = x.transpose()
    for i in range(0, numIterations):
        hypothesis = np.dot(x, theta)
        loss = hypothesis - y
        # avg cost per example (the 2 in 2*m doesn't really matter here.
        # But to be consistent with the gradient, I include it)
        cost = np.sum(loss ** 2) / (2 * m)
        print("Iteration %d | Cost: %f" % (i, cost))
        # avg gradient per example
        gradient = np.dot(xTrans, loss) / m
        # update
        theta = theta - alpha * gradient
    return theta


genData = polyglot.eval ("""function(numPoints, bias, variance) {
    library(numpy)
    x <- np.zeros(shape=(numPoints, 2))
    y <- np.zeros(shape=numPoints)
    for (i in range(0, numPoints)
    {
     x[i][0] <- 1
     x[i][1] <- i
     y[i] <- (i + bias) + runif(min=0, max=1) * variance
    }
    return (x, y)
}""", language="R")

# gen 100 points with a bias of 25 and 10 variance as a bit of noise
x, y = genData(100, 25, 10)
m, n = np.shape(x)
numIterations= 100000
alpha = 0.0005
theta = np.ones(n)
theta = gradientDescent(x, y, theta, alpha, m, numIterations)
print(theta)