Question

我正在尝试通过机器学习在Python中实现斐波那契数列。我希望我的程序预测给定输入后的下5位数字。例如，如果我通过[0,1,1]，它将预测并返回[2,3,5,8,13]。但是，我找不到解决方法。我的程序目前只能预测下一位数字。是的，我可以对其进行硬编码，使用新的输出来更新数组，但是我不想这样做。我的代码：

#! /usr/bin/python3
from sklearn import svm
from sklearn.linear_model import LinearRegression

features = [
 [0,1,1],
 [2,3,5],
 [8,13,21],
 [34,55,89],

 ]
labels = [2,8,34,144]

clf = LinearRegression()
clf.fit(features, labels)
test = [[144, 233, 377]]
print(clf.predict(test))

有帮助吗？

Answer 1

这可能会帮助您；参见代码中的注释

from sklearn.linear_model import LinearRegression

#define your inputs
features = [ [0,1,1],
             [2,3,5],
             [8,13,21],
             [34,55,89] ]

labels = [2,8,34,144]

# create your linear regression extrapolator
clf = LinearRegression()
clf.fit(features, labels)

# create a simple function to find the next number in the fibonacci sequence
def find_next(feat_list):
    # feat_list is your input list of numbers
    result = clf.predict(feat_list)
    result = result.tolist()
    result = [int(x) for x in result]
    return result

# create one more function to iterate and add as many numbers to the sequence as you want
def find_next_numbers(feat_list, how_many):
    # feat_list is your input list of numbers
    # how_many is the number of numbers you want to append
    result = []
    for i in range(how_many):
        nextnum = find_next(feat_list)
        result = result + nextnum
        # remove the smallest number and add the number you just found
        # before you iterate again using this new list as input
        feat_list[0] = feat_list[0][1:] + nextnum
    return result


# test it
test = [[144, 233, 377]]    
print(find_next_numbers(test, 5))

Answer 2

如果要从模型中获得多个输出，则必须以这种方式进行训练。然后，这成为一个多输出问题，您需要提供3个特征并希望预测5个输出。

有关my answer here的一些说明。

当前，您正在训练它以预测单个值。因此，模型将始终预测单个值。通过在输出中输入多个值来训练模型。

类似这样的东西：

# Three features per row
features = [[0,   1,  1],
            [2,   3,  5],
            [8,  13, 21],
            [34, 55, 89]]

# This changed.
# Now a single label consists of a list of output values to be predicted
# 5 outputs per row
labels = [[2,     3,   5,   8,  13], 
          [8,    13,  21,  34,  55], 
          [34,   55,  89, 144, 233], 
          [144, 233, 377, 610, 987]]

clf = LinearRegression()
clf.fit(features, labels)
test = [[144, 233, 377]]
print(clf.predict(test))

# Output
# array([[ 610.,  987., 1597., 2584., 4181.]])

但是请注意，正如我在链接的答案中提到的那样，所有scikit-learn估计器都无法预测多个输出。

如何通过Scikit-Learn在Python中实现斐波那契序列？

2 个答案: