BrambleXu

import math, gzip, pickle
import numpy as np
import random

import torch
from torch import tensor
from torch.nn import init

from fastai import datasets

BrambleXu

import numpy as np
import matplotlib.pyplot as plt

# random seed to make sure reimplement
np.random.seed(0)

# the real model line
def g(x):
    return 0.1 * (x + x**2 + x**3)

BrambleXu

import numpy as np
import matplotlib.pyplot as plt

# random seed to make sure reimplement
np.random.seed(0)

# the real model line
def g(x):
    return 0.1 * (x + x**2 + x**3)

BrambleXu

import math
import numpy as np
import matplotlib.pyplot as plt

# read data
data = np.loadtxt("non_linear_data.csv", delimiter=',', skiprows=1)
train_x = data[:, 0:2]
train_y = data[:, 2]

# plot data points

BrambleXu

import numpy as np
import matplotlib.pyplot as plt

# read data
data = np.loadtxt("non_linear_data.csv", delimiter=',', skiprows=1)
train_x = data[:, 0:2]
train_y = data[:, 2]

# plot data points
# plt.plot(train_x[train_y == 1, 0], train_x[train_y == 1, 1], 'o')

BrambleXu

BrambleXu

import numpy as np
import matplotlib.pyplot as plt

# read data
data = np.loadtxt("linear_data.csv", delimiter=',', skiprows=1)
train_x = data[:, 0:2]
train_y = data[:, 2]

# initialize parameter
theta = np.random.randn(3)

BrambleXu

BrambleXu

class Solution:
    def removeDuplicates(self, nums: List[int]) -> int:
        if len(nums) == 0: return 0
        if len(nums) == 1: return 1
        
        # nums = [0,0,1,1,1,2,2,3,3,4]
        j = 1 # slover pointer, only move when meet unique number
        for i in range(1, len(nums)): # faster pointer, i will iterate over all element in nums
            if nums[i] != nums[i-1]: # when nums[i] is a unique number, assign it to nums[j]
                nums[j] = nums[i]

BrambleXu

var fs = require('fs');
var text = fs.readFileSync('./unicode_plane.txt');
var lines = text.toString().split('\n')

for (i=0; i < lines.length; i++) {
    // console.log(lines[i].split(' '));
    unicode_char = lines[i].split(' ')[0];
    count = lines[i].split(' ')[1];
    
    // test for not use spread operator