mutolisp

% BEGIN of HW3
% psilotum 2010/03/13
% for GNU Octave 

%% 1. Compute the mean and SE(mean) for the fish and copepod densities respectively,
% using both normal theory and non-parametric bootstrap

% import data first, and select the fish density
% and copepod density
enviANDdensity=xlsread("enviANDdensity.csv");

mutolisp

% mybootstrap function
% psilotum 2010/03/13
% for GNU Octave 

%% obj is the object, n is the final number
%  and i is the bootstrapping repeat time

function[bootstrp] = mybootstrap(obj,n,i)

if columns(obj)==1

mutolisp

#!/usr/bin/env bash

# # create target database
# psql -d bird -q -c "
# DROP TABLE grid;
# CREATE TABLE grid
# (
#  gid integer
# ) WITH (OIDS=FALSE);
# "

mutolisp

#!/usr/bin/env python

import random
pres=[2,3,4,6,8,10,11]
result=[]
for i in range(1,12):
    if i not in pres:
            result.append(i)
    else:
            continue

mutolisp

%% 2. Randomization test whether significant difference exists between 
%  the density of Oncaea Venusta and Canthocalanus pauper. (Assume all 
%  data are independent and use all 34 stations.) Report your p-value.

% sp1 is Oncaea venusta, sp2 is canthocalanus pauper
sp1=copepod_comp(169,:);
sp2=copepod_comp(15,:);

mu_sp12=mean(sp1-sp2);

mutolisp

% Classification using multilayer perceptron
% Use the data provided (modeldata.txt). The first 
%    two columns are x1 and x2, the column 3 to 5 represent 
%    coding for three class (y).
% Write your own MLP. Use off-line learning, learning 
%    rate=0.001, learning time=1000 step, # of hidden neuron=5,
%    use tanh as your activation function. Plot MSE vs learning 
%    steps. Calculate the min(MSE) and associated optimal weights.
% (NOTE: you should try different initial conditions several times 
% to check whether you get trapped in the local minimum.)

mutolisp

rcetacean=load("cetaceancorrect.dat.txt");
n=size(rcetacean)(:,1);
entangle=rcetacean(rcetacean(:,4)==1,:);
calm=rcetacean(rcetacean(:,2)<=3);
rough=rcetacean(rcetacean(:,2)>=4);
% calculate the dimension of calm or rough
cs=size(calm)(:,1);
rs=size(rough)(:,1);
% if calm seas=0 (value 0 to 3) else rough seas=1
% (value >= 4)

mutolisp

% Q1: Fit a maximum likelihood model to cetacean entanglement 
%  problem in homework 14 and use AIC to determine the most
%  appropriate model (i.e. compare models:
% 1. Full model (with beta0 beta1 beta2 beta3) 
% 2. Model without beta1, 
% 3. Model without beta2, 
% 4. Model without beta3
% 5. Full model adding interaction term: x*y.

clear;clc;

mutolisp

#!/usr/bin/env bash

# Lin, Cheng-Tao 2010
# School of Forestry and Resource Conservation, National Taiwan University
# For GRASS GIS

# Variable explanations, derived from  http://www.worldclim.org/bioclim-aml
# BIO1 = Annual Mean Temperature
# BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
# BIO3 = Isothermality (P2/P7) (* 100)

mutolisp

Dansk Botanisk Arkiv 9(3): 30, t. 3, f. 7-8. 1937. (Dansk Bot. Ark.)
Icones plantarum formosanarum nec non et contributiones ad floram formosanam. 5: 256. 1915. (Icon. Pl. Formosan.)
Botanical Magazine 26(304): 112-113. 1912. (Bot. Mag. (Tokyo))
Bulletin du Jardin Botanique de Buitenzorg, ser. 2, 2(7): 39, pl. 5, f. 1-2. 1912. (Bull. Jard. Bot. Buitenzorg, ser. 2,)

以上是我想處理的文字，主要分成四個部分：
1. 最前面文字的部份(期刊、書名) 2. 卷/期/頁數等資訊 3. 年份 4. 縮寫
規則如下：
A. 最前面文字的部份擷取出來，當成一個欄位(用 pipeline 分隔)
  A1. 例外：若有期刊書名為 Sometext text, ser. 4, 時，連同", ser. 4"擷取此部份