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import sys
import requests
from lxml import etree

if __name__=="__main__":
    smiles = sys.argv[1]

    html_doc = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" + smiles + "/record/XML")
    root = etree.XML(html_doc.text)

tobigithub

#!/usr/bin/env python2
import numpy as np
import sys

elements = dict()
elements["H"] = 1
elements["C"] = 6
elements["N"] = 7
elements["O"] = 8
elements["F"] = 9

tobigithub

! PUBLIC DOMAIN LICENSE 2017 BY ANDERS S. CHRISTENSEN
!
! I WROTE THIS BECAUSE I WAS BORED - I DON'T RECOMMEND
! WRITING FILE PARSERS IN FORTRAN BECAUSE IT IS NOT
! PRODUCTIVE.

program convert
    
    implicit none

tobigithub

################################### Keras2DML: Parallely training neural network with SystemML####################################### 
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Input, Dense, Conv1D, Conv2D, MaxPooling2D, Dropout,Flatten
from keras import backend as K
from keras.models import Model
import numpy as np
import matplotlib.pyplot as plt

tobigithub

#' Basketball: A function to run a lot of different models
#'
#' This function allows you to run a lot of different models from the caret package
#' @param method The methods to use i.e c("glm", "rf")
#' @param formula The formula to use
#' @param data The data frame to use. Can leave blank if supplying independent training and testing
#' @param regression Logical TRUE if type of model is "regression"
#' @param metric Metic to use. For example, "RMSE" if regression and "Accuracy" if classification
#' @param Train Do you want to supply a training set bypassing the partitioning
#' @param Test If you bypassed partition need to also supply testing set

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#' Advanced Summary Statistics Table with Description Option
#'
#' This function allows you to output a summary table much like tabstat. Statistics are based on the columns/variables of the dataframe. Only works when the number of columns is greater than 1.
#' @param data.frame Data frame object
#' @param variables Number or vector of variables/ variable names to include
#' @param table Type of table to return. Options are "simple"/"table" or "latex"/"xtable"
#' @param caption Caption to include underneath the table if "latex"/"xtable"
#' @param digits Digits to display if "latex"/"xtable"
#' @param stats Statistics to include: 'count','sum','max','min','range','sd','var', 'cv','semean','skewness','kurtosis', 'q1','q5','q10','q25','median', 'q75','q90','q95','q99','iqr'
#' @param description Further description for each variable to be included in the table

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#' Load packages and install packages that are not installed before loading
#'
#' This function allows you to check install packages then load
#' @param packages Vector of packages names to be installed
#' @param dependencies Logical. Return package dependencies?
#' @param repos Repository for install.packages()
#' @keywords install.packages
#' @keywords require
#' @keywords library 
#' @export

tobigithub

# Example of training a glm model on a spam data-set, using the caret library.
library(caret)
library(kernlab)

# Load spam dataset.
data(spam)

# Split the data into a training/test set by 60% training/40% test.
inTrain <- createDataPartition(y = spam$type, p=0.6, list=FALSE)
training <- spam[inTrain,]

tobigithub

# Quiz 4

# Question 1.
library(ElemStatLearn)
library(randomForest)
library(caret)

data(vowel.train)
data(vowel.test)

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# Question 1
library(AppliedPredictiveModeling)
library(caret)

data(segmentationOriginal)

set.seed(125)

#inTrain <- createDataPartition(segmentationOriginal$Case, list=FALSE)
inTrain <- data$Case == "Train"