函数练习题——腾讯笔试编程题

题目需求:对于一个十进制的正整数,定义f(n)为其各位数字的平方和,如:

                   f(13)=1**2+3**2=10

                   f(207)=2**2+0**2+7**2=53

下面给出三个正整数k,a,b,你需要计算有多少个正整数n满足a<=n<=b,且k*f(n)=n

例如:输入:第一行包含3个正整数k,a,b,k>=1,a,b<=10**18,a<=b

           输出:输出对应的答案;

范例:输入:51 5000 10000

           输出:3

def f(n):
    n = str(n)  # 1.先把数字转换为字符串
    sum = 0
    for i in n:
        sum += int(i) ** 2
    return sum      # 2.计算字符串中每个数的平方
s = raw_input('请输入(k,a,b):')    # 1.接收变量
li = []
for item in s.split():
    li.append(int(item))
k, a, b = li
count = 0   # 2.进行判断是否满足条件
for i in range(a, b + 1):
    if k * f(i) == i:
        count += 1
print count

Programming Exercise 1: Linear Regression Machine Learning Introduction In this exercise, you will implement linear regression and get to see it work on data. Before starting on this programming exercise, we strongly recom- mend watching the video lectures and completing the review questions for the associated topics. To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise. You can also find instructions for installing Octave/MATLAB in the “En- vironment Setup Instructions” of the course website. Files included in this exercise ex1.m - Octave/MATLAB script that steps you through the exercise ex1 multi.m - Octave/MATLAB script for the later parts of the exercise ex1data1.txt - Dataset for linear regression with one variable ex1data2.txt - Dataset for linear regression with multiple variables submit.m - Submission script that sends your solutions to our servers [?] warmUpExercise.m - Simple example function in Octave/MATLAB [?] plotData.m - Function to display the dataset [?] computeCost.m - Function to compute the cost of linear regression [?] gradientDescent.m - Function to run gradient descent [†] computeCostMulti.m - Cost function for multiple variables [†] gradientDescentMulti.m - Gradient descent for multiple variables [†] featureNormalize.m - Function to normalize features [†] normalEqn.m - Function to compute the normal equations ? indicates files you will need to complete † indicates optional exercises
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