{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime\n", "import glob\n", "import pandas as pd\n", "import csv\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-5439.653663160256 -8354.182366165229 sa-random-unigram-uniform.csv\n", "-5439.653663160256 -8259.44168109899 hillclimbing-random-unigram-uniform.csv\n" ] } ], "source": [ "for f in glob.glob(\"*unigram*.csv\"):\n", " df = pd.read_csv(f)\n", " print(df.fitness.max(), df.fitness.min(), f)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-14681.308607565503 -27211.09615617547 hillclimbing-random-trigram-uniform.csv\n", "-14681.308607565503 -17464.568516864027 hillclimbing-given-trigram-uniform.csv\n", "-14681.308607565503 -21515.898852481398 sa-given-trigram-gaussian.csv\n", "-14681.308607565503 -17464.568516864027 hillclimbing-given-trigram-gaussian.csv\n", "-14681.308607565503 -28346.7456787418 sa-random-trigram-uniform.csv\n", "-14681.308607565503 -21065.204759662218 sa-given-trigram-uniform.csv\n" ] } ], "source": [ "for f in glob.glob(\"*trigram*.csv\"):\n", " df = pd.read_csv(f)\n", " print(df.fitness.max(), df.fitness.min(), f)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", " | \n", " | fitness | \n", "
---|---|---|
worker | \n", "iteration | \n", "\n", " |
0 | \n", "0 | \n", "-23391.198596 | \n", "
500 | \n", "-16429.246383 | \n", "|
1000 | \n", "-15210.110480 | \n", "|
1500 | \n", "-14895.824563 | \n", "|
2000 | \n", "-14681.308608 | \n", "|
2500 | \n", "-14681.308608 | \n", "|
3000 | \n", "-14681.308608 | \n", "|
3500 | \n", "-14681.308608 | \n", "|
4000 | \n", "-14681.308608 | \n", "|
4500 | \n", "-14681.308608 | \n", "|
5000 | \n", "-14681.308608 | \n", "|
5500 | \n", "-14681.308608 | \n", "|
6000 | \n", "-14681.308608 | \n", "|
6500 | \n", "-14681.308608 | \n", "|
7000 | \n", "-14681.308608 | \n", "|
7500 | \n", "-14681.308608 | \n", "|
8000 | \n", "-14681.308608 | \n", "|
8500 | \n", "-14681.308608 | \n", "|
9000 | \n", "-14681.308608 | \n", "|
9500 | \n", "-14681.308608 | \n", "|
10000 | \n", "-14681.308608 | \n", "|
10500 | \n", "-14681.308608 | \n", "|
11000 | \n", "-14681.308608 | \n", "|
11500 | \n", "-14681.308608 | \n", "|
12000 | \n", "-14681.308608 | \n", "|
12500 | \n", "-14681.308608 | \n", "|
13000 | \n", "-14681.308608 | \n", "|
13500 | \n", "-14681.308608 | \n", "|
14000 | \n", "-14681.308608 | \n", "|
14500 | \n", "-14681.308608 | \n", "|
... | \n", "... | \n", "... | \n", "
23 | \n", "5000 | \n", "-14681.308608 | \n", "
5500 | \n", "-14681.308608 | \n", "|
6000 | \n", "-14681.308608 | \n", "|
6500 | \n", "-14681.308608 | \n", "|
7000 | \n", "-14681.308608 | \n", "|
7500 | \n", "-14681.308608 | \n", "|
8000 | \n", "-14681.308608 | \n", "|
8500 | \n", "-14681.308608 | \n", "|
9000 | \n", "-14681.308608 | \n", "|
9500 | \n", "-14681.308608 | \n", "|
10000 | \n", "-14681.308608 | \n", "|
10500 | \n", "-14681.308608 | \n", "|
11000 | \n", "-14681.308608 | \n", "|
11500 | \n", "-14681.308608 | \n", "|
12000 | \n", "-14681.308608 | \n", "|
12500 | \n", "-14681.308608 | \n", "|
13000 | \n", "-14681.308608 | \n", "|
13500 | \n", "-14681.308608 | \n", "|
14000 | \n", "-14681.308608 | \n", "|
14500 | \n", "-14681.308608 | \n", "|
15000 | \n", "-14681.308608 | \n", "|
15500 | \n", "-14681.308608 | \n", "|
16000 | \n", "-14681.308608 | \n", "|
16500 | \n", "-14681.308608 | \n", "|
17000 | \n", "-14681.308608 | \n", "|
17500 | \n", "-14681.308608 | \n", "|
18000 | \n", "-14681.308608 | \n", "|
18500 | \n", "-14681.308608 | \n", "|
19000 | \n", "-14681.308608 | \n", "|
19500 | \n", "-14681.308608 | \n", "
960 rows × 1 columns
\n", "