ChatGPT python code for distribution fitting

 Posted on December 12, 2023  |  2 minutes  |  291 words  |  Package Build

I asked ChatGPT for some python code to do distribution fitting and this is what it provided. I think I needed to edit it to fix some errors but it’s a nice solution. First, I wanted to put in some R code that will allow me to display the results in blogdown, which is what I’m using to post to this site. library(knitr) library(reticulate) knitr::knit_engines$set(python = reticulate::eng_python) The following python code was used for distribution fitting. [Read More]
Data Analytics  Python 

Coursera - introduction to tensorflow

 Posted on August 3, 2022  |  3 minutes  |  428 words  |  Teddy Wong

Week 1 Assignment: Housing Prices In this exercise you’ll try to build a neural network that predicts the price of a house according to a simple formula. Imagine that house pricing is as easy as: A house has a base cost of 50k, and every additional bedroom adds a cost of 50k. This will make a 1 bedroom house cost 100k, a 2 bedroom house cost 150k etc. How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc. [Read More]
Python  Tensorflow  ML 

Python - Handling Exceptions

 Posted on August 3, 2022  |  1 minutes  |  205 words  |  Teddy Wong

From: LinkedIn course ‘Python Essential Training’ by Ryan Mitchell https://www.linkedin.com/learning/python-essential-training-14898805 Try, except, finally import time as time def causeError(): start = time.time() #set start timer try: #delay run by 0.5 secs time.sleep(0.5) return 1/0 except Exception: print('There was some sort of error!') finally: print(f'Function took {time.time() - start} seconds to execute') causeError() ## There was some sort of error! ## Function took 0.5048558712005615 seconds to execute Custom Decorators *args **kwargs are multiple arguments or string arguments. [Read More]
Python 

Python - Multithreading/Multiprocessing

 Posted on August 3, 2022  |  3 minutes  |  589 words  |  Teddy Wong

From: LinkedIn course ‘Python Essential Training’ by Ryan Mitchell https://www.linkedin.com/learning/python-essential-training-14898805 import threading import time Threads def longSquare(num): time.sleep(1) return num**2 [longSquare(n) for n in range(0, 5)] ## [0, 1, 4, 9, 16] t1 = threading.Thread(target=longSquare, args=(1,)) #args is tuple t2 = threading.Thread(target=longSquare, args=(2,)) t1.start() t2.start() t1.join() t2.join() def longSquare(num, results): time.sleep(1) results[num] = num**2 results = {} t1 = threading.Thread(target=longSquare, args=(1, results)) #args are tuples t2 = threading.Thread(target=longSquare, args=(2, results)) t1. [Read More]
Python 

Python - opening reading writing files

 Posted on August 3, 2022  |  2 minutes  |  362 words  |  Teddy Wong

From: LinkedIn course ‘Python Essential Training’ by Ryan Mitchell https://www.linkedin.com/learning/python-essential-training-14898805 reading files f = open('some_file.txt','r') print(f) #gets file type, need to read the file f.readline() f.readlines() #puts lines into list of strings for line in f.readlines(): print(line.strip()) # strips leading and trailing spaces writing files f = open('somefiles.txt','w') # creates a file f.write('Line 1\n') f.write('Line 2\n') f.close() # python doesn't write until you close or run out of buffer and will overwrite existing text appending files [Read More]
Python 

Python Neural Network Basics

 Posted on August 3, 2022  |  1 minutes  |  177 words  |  Teddy Wong

From https://iamtrask.github.io/2015/07/12/basic-python-network/ import numpy as np sigmoid function def nonlin(x,deriv=False): if(deriv==True): return x*(1-x) return 1/(1+np.exp(-x)) input dataset X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ]) output dataset y = np.array([[0,0,1,1]]).T seed random numbers to make calculation deterministic (just a good practice) np.random.seed(1) initialize weights randomly with mean 0 syn0 = 2*np.random.random((3,1)) - 1 print(syn0) ## [[-0.16595599] ## [ 0.44064899] ## [-0.99977125]] variables l0 is input layer l1 is hidden layer l1_error is the loss function l1_delta is the gradient descent function for calculating the back-propagation syn0 are synapses, weights between l0 and l1, and also how the weights are updated are shown. [Read More]
Python 

Codecademy - Pandas Lesson

 Posted on July 28, 2022  |  25 minutes  |  5258 words  |  Teddy Wong

You’re getting ready to staff the clinic for March this year. You want to know how many visits took place in March last year, to help you prepare. Write a command that will produce a Series made up of the March data from df from all four clinic sites and save it to the variable march. #import /;../,codecademylib3 import pandas as pd df = pd.DataFrame([ ['January', 100, 100, 23, 100], ['February', 51, 45, 145, 45], ['March', 81, 96, 65, 96], ['April', 80, 80, 54, 180], ['May', 51, 54, 54, 154], ['June', 112, 109, 79, 129]], columns=['month', 'clinic_east', 'clinic_north', 'clinic_south', 'clinic_west']) print(df) ## month clinic_east clinic_north clinic_south clinic_west ## 0 January 100 100 23 100 ## 1 February 51 45 145 45 ## 2 March 81 96 65 96 ## 3 April 80 80 54 180 ## 4 May 51 54 54 154 ## 5 June 112 109 79 129 # integer location within dataframe # locations are zero indexed and doesn't include the ending integer march = df. [Read More]
Python 

Quarto

Weave text and code and render into many outputs

 Posted on July 15, 2022  |  1 minutes  |  193 words  |  Teddy Wong

Checking out Quarto today. It looks like a more comprehensive tool than R Markdown though it does a lot of similar things like being able to write text and include code cells (like Jupyter), but then also able to render the output into many formats (pdf, html, word, etc) using Pandoc. I guess that makes sense since it’s published by the same folks behind R Markdown and R Studio: [Read More]
Quarto  R  Python  Jupyter  Observable JS  Julia  Pdf  HTML