deven96

deven96

RileyLazarou

# 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)

RileyLazarou

# 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):
    """

RileyLazarou

# 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],

RileyLazarou

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)

bogsio

# 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

mirrorquill

Data Science Process

Steps to take for a comprehensive analysis.

Stage 1: Define

Project background.

Step 1.1: Describe.

victor-iyi

" File: ~/.vimrc
"
" Author: Victor I. Afolabi

syntax enable

colorscheme desert
" highlight Normal guibg=none

" =============================================================================

toksdotdev

Someone asked a question some weeks back about why size_of::<Option<T>> is always double. Answer is because of alignment.

Explanation

How are Rust enum represented in C?

C doesn't have the ability to directly represent complex Rust enum, hence, the need for a workaround. To understand that, let's take a look at how Option<i32> is represnted in C.

E.g. Given: