Jason's Blog

This example shows the performances of extremely randomized trees, k nearest neighbors, linear regression, and ridge regression in model estimation and prediction for face completion, i.e. predicting the lower half of a face given the upper half. The dataset is "fetch_olivetti_faces" coming from sklearn library. From scikit-learn library, we should import: extremely randomized trees: ensemble.ExtraTreeRegressor k nearest neighbors: neighbors.KNeighborsRegressor linear regression: linear_model.LinearRegression ridge regression: linear_model.RidgeCV The example code is as follows: # face completion print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import fetch_olivetti_faces from sklearn.utils.validation import check_random_state from sklearn.ensemble import ExtraTreesRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.linear_model import LinearRegression from sklearn.linear_model import RidgeCV ...

4. Median of Two Sorted Arrays There are two sorted arrays  nums1  and  nums2  of size m and n respectively. Find the median of the two sorted arrays. Example 1: nums1 = [1, 3] nums2 = [2] The median is 2.0 Example 2: nums1 = [1, 2] nums2 = [3, 4] The median is (2 + 3)/2 = 2.5 The idea is to pick up the numbers in nums2 that are smaller than a number in nums1, until we get the median. Pay attention: 1. consider three cases: odd, even, and null; 2. max of nums1 may smaller than min of nums2; 3. use float() while doing the division. My code in Python is as follows: class Solution(object):     def findMedianSortedArrays(self, nums1, nums2):         """         :type nums1: List[int]         :type nums2: List[int]         :rtype: float         """         m = len(nums1)         n = len(nums...

Unfortunately, there is no weighted built-in functions in Python. If we want to get some weighted percentiles by Python, one possible method is to extend the list of data, letting the values of weight as the numbers of elements, which is discussed in a Stack Overflow poster . For example, if we have a data like, score   weight 5          2 4          3 2          4 8          1 we firstly extend the list of scores to {5, 5, 4, 4, 4, 2, 2, 2, 2, 8}, and then find the percentiles such as 10% or 50% percentile. The limitations of this method are, (1) weight must be integers; (2) values of weight cannot be very large. What if we want to calculate the weighted percentiles of a large dataset with very large non-integer weights? In this article, I want to show you an alternative method, under Python pandas. step1: given percentile q, (0<=q<=1), calculate p = q * sum of wei...