scidam

# we assume the set of rules is a Python list;
# native Python set-type isn't suitable to store the rules
rules = [rule1, rule2, rule3]

def score(rule, *args, **kwargs):
    # args, kwargs  -- some additional parameters, if needed..
    pass 
    # Implement score function for an individual rule here... 

scidam

# It is highly recommended to use Pandas for such data processing problems
import pandas as pd
import numpy as np

dataset = {'feature1': np.random.rand(5000),
                 'feature2': np.random.rand(5000),
                 'feature3': np.random.rand(5000)
                 }

scidam

'''
Hi, Nadeem!

Let's dive in deep how the most of clustering algorithms work. This allows us
to find what kind of encoding should be used to handle such categorical data.

If we consider hierarchical clustering algorithms, they use distances between
objects/items to be clustered and form groups according some linkage rules.
So, distances between objects are very important when do clustering.

scidam

import numpy as np 

lat_min = 45.0
lat_max = 60.0
lon_min = 85.0
lon_max = 100.0
density = 3 # three points per `squared` degree
import numpy as np 

np.random.seed(30) # for reproducibility, this line could be deleted

scidam

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_clipboard(header=None)"

scidam

scidam

# occurrence table plot -- species  (load it from csv)
table1 <- read.table("table1.csv", sep=";")

table2 <- read.table("table2.csv", sep=";")

# talble of features sp -- feature (load data from csv)
# table2 <- cbind(c(1,1,0),c(0,0,1), c(0,1,0))

scidam

'''
Based on: https://www.kaggle.com/dingdiego/u-net-batchnorm-augmentation-stratification

U-Net: Convolutional Networks for Biomedical Image Segmentation
(https://arxiv.org/abs/1505.04597)
---
img_shape: (height, width, channels)
out_ch: number of output channels
start_ch: number of channels of the first conv
depth: zero indexed depth of the U-structure

scidam

class Node:
    def __init__(self, value, type):
        self.value = value
        self.type = type
        
intervals = [[2, 10], [3, 7], [4, 7], [6, 8], [8, 9]]
nodes = list()
for interval in intervals:
    nodes.append(Node(interval[0],'l'))
    nodes.append(Node(interval[1],'r'))

scidam

cd /code
COM_PATH=/mnt/c/odm

for prjname in ussurisky;
  do
    cd /code
    ./run.sh --project-path $COM_PATH $prjname --max-concurrency 3 --dtm --dsm --use-exif --verbose
    tar -C $COM_PATH --exclude=$prjname/images --exclude=$prjname/opensfm -zcvf  $COM_PATH/$prjname.tar.gz $prjname