devforfu

from io import BytesIO
from pprint import pprint as pp

import requests
from PIL import Image
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

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# training loop parameters
lr = 1e-3
wd = 1e-5
bs = 2000
n_epochs = 100
patience = 10
no_improvements = 0
best_loss = np.inf
best_weights = None
history = []

devforfu

class EmbeddingNet(nn.Module):
    """
    Creates a dense network with embedding layers.
    
    Args:
    
        n_users:            
            Number of unique users in the dataset.

        n_movies: 

devforfu

X, y = reader.dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, train_size=0.8)

pipeline = make_pipeline(
    BatchTransformer(StandardScaler()),
    KMeansQuantization(k=2),
    RandomForestClassifier(n_estimators=500))

pipeline.fit(X_train, y_train)
y_preds = pipeline.predict(X_test)

devforfu

"""
Main repository: https://github.com/devforfu/Blog/tree/master/trees
"""
import os
from os.path import join
from pprint import pprint

import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

devforfu

"""
Wrist-Worn Accelerometer Dataset using scikit-learn.
"""

import re
from os import listdir
from os.path import exists, join, basename, isdir

import numpy as np

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from os.path import join

import numpy as np
from sklearn.model_selection import train_test_split

from quantization import quantize
from decision_tree import learn_tree
from decision_tree import predict_tree
from ensemble import RandomForestClassifier
from utils import train_test_split, encode_labels

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class RandomForestClassifier:

    def __init__(self, tree_funcs, n_trees: int=10,
                 feature_subset_size: str='sqrt', max_depth: int=5,
                 min_split_size: int=10, min_leaf_size: int=None,
                 log=None):

        if n_trees < 1:
            raise ValueError(f'cannot build an ensemble of {n_trees:d} trees')

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"""
Simple decision tree implementation using Numpy.
"""
from collections import Counter

import numpy as np


class _Node:
    """

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def quantize(dataset_path, k):
    dataset = []
    
    for points in read_files(dataset_path):
        x = np.asarray(points, dtype=np.float)
        centroids, _ = kmeans(x, n_clusters=k)
        feature_vector = centroids.flatten()
        dataset.append(feature_vector)

    return np.array(dataset)