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from Project.DenseNet.densenet import densenet121_model
from Project.ss150.ss150 import DataGenerator
from keras.utils.io_utils import HDF5Matrix
from keras.utils import multi_gpu_model
from keras.optimizers import SGD


from keras import backend as K
from sklearn import metrics
import random

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FNs

FP

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from keras.utils.io_utils import HDF5Matrix
import random
import numpy as np

random.seed(52)

sub_sample = 0.01

data_path = '/home/naan/SnapshotSerengeti/data/SnapshotSerengeti224.h5'

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def remove_repeats(df):
    df = df.assign(is_giraffe=lambda x: x.Species == 'giraffe')
    df = df.sort_values('is_giraffe')
    df = df.drop_duplicates(['URL_Info'], keep='last')
    return df

clean_df = remove_repeats(test)


!ls -1 '/home/naan/SnapshotSerengeti/data/resized/' | wc -l 

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import csv
import os.path
import itertools
from collections import defaultdict

data_path = '/home/naan/SnapshotSerengeti/data/'

def raw_sampler(rawfile, outfile, n=500):
    raw_path = os.path.join(data_path, rawfile)
    sample_path = os.path.join(data_path, outfile)

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import csv
from collections import defaultdict
#rawdate is format of ['CaptureEventID', 'ClassificationID', 'UserID', 'Species', 'Count', 'Standing', 'Resting', 'Moving', 'Eating', 'Interacting', 'Babies']
#requires raw_data, and all_images


#lets remove the instances where there is more than 1 animal in a still and when there is a human
with open('D:\SnapshotSerengeti\consensus_data.csv', newline='') as rawdata,\
        open('D:\SnapshotSerengeti\curated.csv','w', newline='') as curated,\
            open('D:\SnapshotSerengeti\\all_images.csv', newline='') as images:

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'use strict';
var Promise = require('bluebird');
var sqlite3 = require('sqlite3');
var db = new sqlite3.Database('testDB.db');
var validator = require('validator');
var has = require('has');

exports.index = function(req, res){
    res.render('register', {title: 'register'});
};

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CFLAGS = -lm -lrt
INCS = parser.h
TARGET = test_parser
SOURCES = parser.c $(TARGET).c
CC = gcc

all: $(TARGET)

$(TARGET): $(SOURCES) $(INCS)
	$(CC) $(SOURCES) -o $(TARGET) $(CFLAGS)

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#!python3 
import random
import tradesimulator as ts 
import sys

behaviourarray = {
            'takerisk': ts.TakeRisk(), 
            'avoidrisk': ts.AvoidRisk(), 
            'findarbs': ts.FindArbs(),
            }

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data = load('ex1data2.txt'); % loads from array of 47x3 (x1, x2, y)  
X = data(:, 1:2); % takes first two columns from the array we loaded

X_norm = X; 
mu = zeros(1, size(X, 2)); % work out mean  for each column in X
sigma = zeros(1, size(X, 2)); % work out stdev for each column in X


for i = 1:size(X,2)
    mu(1,i) = mean(X(:,i)) % find mean for each column of X