Steps to take for a comprehensive analysis.
Project background.
# Load the data | |
df = pd.read_csv('iris.csv') | |
# Enumerate the classes | |
unique_classes = sorted(list(set(df['variety']))) | |
class_number = {y : x for x,y in enumerate(unique_classes)} | |
df['variety'] = [class_number[x] for x in df['variety']] | |
# Convert to numpy array and standardize the features | |
data_X = df[['sepal.length', 'sepal.width', 'petal.length', 'petal.width']].values | |
data_Y = df[['variety']].values | |
data_X = data_X - np.min(data_X, axis=0) |
# Set up the environment and collect the observation space and action space sizes | |
env = gym.make("CartPole-v1") | |
observation_space = env.observation_space.shape[0] | |
action_space = env.action_space.n | |
# The function for creating the initial population | |
organism_creator = lambda : Organism([observation_space, 16, 16, 16, action_space], output='softmax') | |
def simulate_and_evaluate(organism, trials=1): | |
""" |
# The function to create the initial population | |
organism_creator = lambda : Organism([1, 16, 16, 16, 1], output='linear') | |
# The function we are trying to learn. numpy doesn't have tau... | |
true_function = lambda x : np.sin(2 * np.pi * x) # | |
# The loss function, mean squared error, will serve as the negative fitness | |
loss_function = lambda y_true, y_estimate : np.mean((y_true - y_estimate)**2) | |
def simulate_and_evaluate(organism, replicates=1): | |
""" | |
Randomly generate `replicates` samples in [0,1], |
import copy | |
import numpy as np | |
class Organism(): | |
def __init__(self, dimensions, use_bias=True, output='softmax'): | |
self.layers = [] | |
self.biases = [] | |
self.use_bias = use_bias | |
self.output = self._activation(output) |
# https://nlpforhackers.io/named-entity-extraction/ | |
import os | |
import string | |
import collections | |
import pickle | |
from collections import Iterable | |
from nltk.tag import ClassifierBasedTagger | |
from nltk.chunk import ChunkParserI, conlltags2tree, tree2conlltags |
" File: ~/.vimrc | |
" | |
" Author: Victor I. Afolabi | |
syntax enable | |
colorscheme desert | |
" highlight Normal guibg=none | |
" ============================================================================= |
# Authors: Mathieu Blondel, Vlad Niculae | |
# License: BSD 3 clause | |
import numpy as np | |
def _gen_pairs(gen, max_iter, max_inner, random_state, verbose): | |
rng = np.random.RandomState(random_state) | |
# if tuple, interpret as randn |