repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
numpy | numpy/typing/tests/data/pass/dtype.py | .py | import numpy as np
dtype_obj = np.dtype(np.str_)
void_dtype_obj = np.dtype([("f0", np.float64), ("f1", np.float32)])
np.dtype(dtype=np.int64)
np.dtype(int)
np.dtype("int")
np.dtype(None)
np.dtype((int, 2))
np.dtype((int, (1,)))
np.dtype({"names": ["a", "b"], "formats": [int, float]})
np.dtype({"names": ["a"], "form... | 58 | 1,070 |
numpy | numpy/typing/tests/data/pass/shape.py | .py | from typing import Any, NamedTuple
import numpy as np
# Subtype of tuple[int, int]
class XYGrid(NamedTuple):
x_axis: int
y_axis: int
# Test variance of _ShapeT_co
def accepts_2d(a: np.ndarray[tuple[int, int], Any]) -> None:
return None
accepts_2d(np.empty(XYGrid(2, 2)))
accepts_2d(np.zeros(XYGrid(2, 2... | 20 | 439 |
numpy | numpy/typing/tests/data/pass/mod.py | .py | import numpy as np
f8 = np.float64(1)
i8 = np.int64(1)
u8 = np.uint64(1)
f4 = np.float32(1)
i4 = np.int32(1)
u4 = np.uint32(1)
td = np.timedelta64(1, "D")
b_ = np.bool(1)
b = bool(1)
f = float(1)
i = 1
AR = np.array([1], dtype=np.bool)
AR.setflags(write=False)
AR2 = np.array([1], dtype=np.timedelta64)
AR2.setflag... | 150 | 1,571 |
numpy | numpy/typing/tests/data/pass/fromnumeric.py | .py | """Tests for :mod:`numpy._core.fromnumeric`."""
import numpy as np
A = np.array(True, ndmin=2, dtype=bool)
B = np.array(1.0, ndmin=2, dtype=np.float32)
A.setflags(write=False)
B.setflags(write=False)
a = np.bool(True)
b = np.float32(1.0)
c = 1.0
d = np.array(1.0, dtype=np.float32) # writeable
np.take(a, 0)
np.take... | 273 | 3,991 |
numpy | numpy/typing/tests/data/pass/nditer.py | .py | import numpy as np
arr = np.array([1])
np.nditer([arr, None])
| 5 | 63 |
numpy | numpy/typing/tests/data/pass/index_tricks.py | .py | from __future__ import annotations
from typing import Any
import numpy as np
AR_LIKE_b = [[True, True], [True, True]]
AR_LIKE_i = [[1, 2], [3, 4]]
AR_LIKE_f = [[1.0, 2.0], [3.0, 4.0]]
AR_LIKE_U = [["1", "2"], ["3", "4"]]
AR_i8: np.ndarray[Any, np.dtype[np.int64]] = np.array(AR_LIKE_i, dtype=np.int64)
np.ndenumerat... | 63 | 1,404 |
numpy | numpy/typing/tests/data/pass/multiarray.py | .py | import numpy as np
import numpy.typing as npt
AR_f8: npt.NDArray[np.float64] = np.array([1.0])
AR_i4 = np.array([1], dtype=np.int32)
AR_u1 = np.array([1], dtype=np.uint8)
AR_LIKE_f = [1.5]
AR_LIKE_i = [1]
b_f8 = np.broadcast(AR_f8)
b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8)
next(b_f8)
b_f8.reset()
b_f8.index
b_... | 78 | 1,379 |
numpy | numpy/_utils/__init__.py | .py | """
This is a module for defining private helpers which do not depend on the
rest of NumPy.
Everything in here must be self-contained so that it can be
imported anywhere else without creating circular imports.
If a utility requires the import of NumPy, it probably belongs
in ``numpy._core``.
"""
import functools
impo... | 96 | 3,477 |
numpy | numpy/_utils/_pep440.py | .py | """Utility to compare pep440 compatible version strings.
The LooseVersion and StrictVersion classes that distutils provides don't
work; they don't recognize anything like alpha/beta/rc/dev versions.
"""
# Copyright (c) Donald Stufft and individual contributors.
# All rights reserved.
# Redistribution and use in sour... | 487 | 13,988 |
numpy | numpy/_utils/_inspect.py | .py | """Subset of inspect module from upstream python
We use this instead of upstream because upstream inspect is slow to import, and
significantly contributes to numpy import times. Importing this copy has almost
no overhead.
"""
import types
__all__ = ['getargspec', 'formatargspec']
# ---------------------------------... | 193 | 7,436 |
numpy | numpy/_utils/_conversions.py | .py | """
A set of methods retained from np.compat module that
are still used across codebase.
"""
__all__ = ["asunicode", "asbytes"]
def asunicode(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('... | 19 | 329 |
numpy | numpy/ctypeslib/__init__.py | .py | from ._ctypeslib import (
__all__,
__doc__,
_concrete_ndptr,
_ndptr,
as_array,
as_ctypes,
as_ctypes_type,
c_intp,
ctypes,
load_library,
ndpointer,
)
| 14 | 193 |
numpy | numpy/ctypeslib/_ctypeslib.py | .py | """
============================
``ctypes`` Utility Functions
============================
See Also
--------
load_library : Load a C library.
ndpointer : Array restype/argtype with verification.
as_ctypes : Create a ctypes array from an ndarray.
as_array : Create an ndarray from a ctypes array.
References
----------
... | 616 | 19,655 |
ML-From-Scratch | setup.py | .py | from setuptools import setup, find_packages
from codecs import open
from os import path
__version__ = '0.0.4'
here = path.abspath(path.dirname(__file__))
# get the dependencies and installs
with open(path.join(here, 'requirements.txt'), encoding='utf-8') as f:
all_reqs = f.read().split('\n')
install_requires = ... | 30 | 1,078 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/autoencoder.py | .py | from __future__ import print_function, division
from sklearn import datasets
import math
import matplotlib.pyplot as plt
import numpy as np
import progressbar
from sklearn.datasets import fetch_mldata
from mlfromscratch.deep_learning.optimizers import Adam
from mlfromscratch.deep_learning.loss_functions import CrossE... | 119 | 4,017 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/restricted_boltzmann_machine.py | .py | import logging
import numpy as np
import progressbar
from mlfromscratch.utils.misc import bar_widgets
from mlfromscratch.utils import batch_iterator
from mlfromscratch.deep_learning.activation_functions import Sigmoid
sigmoid = Sigmoid()
class RBM():
"""Bernoulli Restricted Boltzmann Machine (RBM)
Parameter... | 83 | 3,357 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/gaussian_mixture_model.py | .py | from __future__ import division, print_function
import math
from sklearn import datasets
import numpy as np
from mlfromscratch.utils import normalize, euclidean_distance, calculate_covariance_matrix
from mlfromscratch.utils import Plot
class GaussianMixtureModel():
"""A probabilistic clustering method for determ... | 122 | 4,723 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/partitioning_around_medoids.py | .py | from __future__ import print_function, division
import numpy as np
from mlfromscratch.utils import normalize, euclidean_distance, Plot
from mlfromscratch.unsupervised_learning import PCA
class PAM():
"""A simple clustering method that forms k clusters by first assigning
samples to the closest medoids, and the... | 124 | 4,900 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/fp_growth.py | .py | from __future__ import division, print_function
import numpy as np
import itertools
class FPTreeNode():
def __init__(self, item=None, support=1):
# 'Value' of the item
self.item = item
# Number of times the item occurs in a
# transaction
self.support = support
# Chi... | 198 | 7,664 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/genetic_algorithm.py | .py | from __future__ import print_function, division
import string
import numpy as np
class GeneticAlgorithm():
"""An implementation of a Genetic Algorithm which will try to produce the user
specified target string.
Parameters:
-----------
target_string: string
The string which the GA should tr... | 105 | 4,227 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/principal_component_analysis.py | .py | from __future__ import print_function, division
import numpy as np
from mlfromscratch.utils import calculate_covariance_matrix
class PCA():
"""A method for doing dimensionality reduction by transforming the feature
space to a lower dimensionality, removing correlation between features and
maximizing the v... | 30 | 1,247 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/dbscan.py | .py | from __future__ import print_function, division
import numpy as np
from mlfromscratch.utils import Plot, euclidean_distance, normalize
class DBSCAN():
"""A density based clustering method that expands clusters from
samples that have more neighbors within a radius specified by eps
than the value min_sampl... | 94 | 4,067 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/dcgan.py | .py | from __future__ import print_function, division
import matplotlib.pyplot as plt
import numpy as np
import progressbar
from sklearn.datasets import fetch_mldata
from mlfromscratch.deep_learning.optimizers import Adam
from mlfromscratch.deep_learning.loss_functions import CrossEntropy
from mlfromscratch.deep_learning.la... | 174 | 6,384 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/apriori.py | .py | from __future__ import division, print_function
import numpy as np
import itertools
class Rule():
def __init__(self, antecedent, concequent, confidence, support):
self.antecedent = antecedent
self.concequent = concequent
self.confidence = confidence
self.support = support
class A... | 191 | 7,906 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/generative_adversarial_network.py | .py | from __future__ import print_function, division
from sklearn import datasets
import math
import matplotlib.pyplot as plt
import numpy as np
import progressbar
from sklearn.datasets import fetch_mldata
from mlfromscratch.deep_learning.optimizers import Adam
from mlfromscratch.deep_learning.loss_functions import CrossE... | 169 | 5,842 |
ML-From-Scratch | mlfromscratch/unsupervised_learning/k_means.py | .py | from __future__ import print_function, division
import numpy as np
from mlfromscratch.utils import normalize, euclidean_distance, Plot
from mlfromscratch.unsupervised_learning import *
class KMeans():
"""A simple clustering method that forms k clusters by iteratively reassigning
samples to the closest centroid... | 92 | 3,530 |
ML-From-Scratch | mlfromscratch/reinforcement_learning/deep_q_network.py | .py | from __future__ import print_function, division
import random
import numpy as np
import gym
from collections import deque
class DeepQNetwork():
"""Q-Learning with deep neural network to learn the control policy.
Uses a deep neural network model to predict the expected utility (Q-value) of executing an action... | 145 | 5,304 |
ML-From-Scratch | mlfromscratch/utils/kernels.py | .py | import numpy as np
def linear_kernel(**kwargs):
def f(x1, x2):
return np.inner(x1, x2)
return f
def polynomial_kernel(power, coef, **kwargs):
def f(x1, x2):
return (np.inner(x1, x2) + coef)**power
return f
def rbf_kernel(gamma, **kwargs):
def f(x1, x2):
distance = np.li... | 21 | 398 |
ML-From-Scratch | mlfromscratch/utils/misc.py | .py | import progressbar
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
import matplotlib.colors as colors
import numpy as np
from mlfromscratch.utils.data_operation import calculate_covariance_matrix
from mlfromscratch.utils.data_operation import calculate_correlation_ma... | 115 | 3,773 |
ML-From-Scratch | mlfromscratch/utils/data_operation.py | .py | from __future__ import division
import numpy as np
import math
import sys
def calculate_entropy(y):
""" Calculate the entropy of label array y """
log2 = lambda x: math.log(x) / math.log(2)
unique_labels = np.unique(y)
entropy = 0
for label in unique_labels:
count = len(y[y == label])
... | 76 | 2,240 |
ML-From-Scratch | mlfromscratch/utils/data_manipulation.py | .py | from __future__ import division
from itertools import combinations_with_replacement
import numpy as np
import math
import sys
def shuffle_data(X, y, seed=None):
""" Random shuffle of the samples in X and y """
if seed:
np.random.seed(seed)
idx = np.arange(X.shape[0])
np.random.shuffle(idx)
... | 168 | 5,089 |
ML-From-Scratch | mlfromscratch/supervised_learning/bayesian_regression.py | .py | from __future__ import print_function, division
import numpy as np
from scipy.stats import chi2, multivariate_normal
from mlfromscratch.utils import mean_squared_error, train_test_split, polynomial_features
class BayesianRegression(object):
"""Bayesian regression model. If poly_degree is specified the features w... | 116 | 4,595 |
ML-From-Scratch | mlfromscratch/supervised_learning/naive_bayes.py | .py | from __future__ import division, print_function
import numpy as np
import math
from mlfromscratch.utils import train_test_split, normalize
from mlfromscratch.utils import Plot, accuracy_score
class NaiveBayes():
"""The Gaussian Naive Bayes classifier. """
def fit(self, X, y):
self.X, self.y = X, y
... | 71 | 3,363 |
ML-From-Scratch | mlfromscratch/supervised_learning/regression.py | .py | from __future__ import print_function, division
import numpy as np
import math
from mlfromscratch.utils import normalize, polynomial_features
class l1_regularization():
""" Regularization for Lasso Regression """
def __init__(self, alpha):
self.alpha = alpha
def __call__(self, w):
retu... | 256 | 10,499 |
ML-From-Scratch | mlfromscratch/supervised_learning/multi_class_lda.py | .py | from __future__ import print_function, division
import matplotlib.pyplot as plt
import numpy as np
from mlfromscratch.utils import calculate_covariance_matrix, normalize, standardize
class MultiClassLDA():
"""Enables dimensionality reduction for multiple
class distributions. It transforms the features space i... | 75 | 2,627 |
ML-From-Scratch | mlfromscratch/supervised_learning/particle_swarm_optimization.py | .py | from __future__ import print_function, division
import numpy as np
import copy
class ParticleSwarmOptimizedNN():
""" Particle Swarm Optimization of Neural Network.
Parameters:
-----------
n_individuals: int
The number of neural networks that are allowed in the population at a time.
model_b... | 130 | 5,985 |
ML-From-Scratch | mlfromscratch/supervised_learning/xgboost.py | .py | from __future__ import division, print_function
import numpy as np
import progressbar
from mlfromscratch.utils import train_test_split, standardize, to_categorical, normalize
from mlfromscratch.utils import mean_squared_error, accuracy_score
from mlfromscratch.supervised_learning import XGBoostRegressionTree
from mlfr... | 105 | 3,726 |
ML-From-Scratch | mlfromscratch/supervised_learning/linear_discriminant_analysis.py | .py | from __future__ import print_function, division
import numpy as np
from mlfromscratch.utils import calculate_covariance_matrix, normalize, standardize
class LDA():
"""The Linear Discriminant Analysis classifier, also known as Fisher's linear discriminant.
Can besides from classification also be used to reduce ... | 44 | 1,395 |
ML-From-Scratch | mlfromscratch/supervised_learning/support_vector_machine.py | .py |
from __future__ import division, print_function
import numpy as np
import cvxopt
from mlfromscratch.utils import train_test_split, normalize, accuracy_score
from mlfromscratch.utils.kernels import *
from mlfromscratch.utils import Plot
# Hide cvxopt output
cvxopt.solvers.options['show_progress'] = False
class Suppor... | 112 | 4,024 |
ML-From-Scratch | mlfromscratch/supervised_learning/random_forest.py | .py | from __future__ import division, print_function
import numpy as np
import math
import progressbar
# Import helper functions
from mlfromscratch.utils import divide_on_feature, train_test_split, get_random_subsets, normalize
from mlfromscratch.utils import accuracy_score, calculate_entropy
from mlfromscratch.unsupervise... | 88 | 3,683 |
ML-From-Scratch | mlfromscratch/supervised_learning/gradient_boosting.py | .py | from __future__ import division, print_function
import numpy as np
import progressbar
# Import helper functions
from mlfromscratch.utils import train_test_split, standardize, to_categorical
from mlfromscratch.utils import mean_squared_error, accuracy_score
from mlfromscratch.deep_learning.loss_functions import SquareL... | 110 | 4,281 |
ML-From-Scratch | mlfromscratch/supervised_learning/adaboost.py | .py | from __future__ import division, print_function
import numpy as np
import math
from sklearn import datasets
import matplotlib.pyplot as plt
import pandas as pd
# Import helper functions
from mlfromscratch.utils import train_test_split, accuracy_score, Plot
# Decision stump used as weak classifier in this impl. of Ada... | 147 | 5,633 |
ML-From-Scratch | mlfromscratch/supervised_learning/logistic_regression.py | .py | from __future__ import print_function, division
import numpy as np
import math
from mlfromscratch.utils import make_diagonal, Plot
from mlfromscratch.deep_learning.activation_functions import Sigmoid
class LogisticRegression():
""" Logistic Regression classifier.
Parameters:
-----------
learning_rate:... | 50 | 2,059 |
ML-From-Scratch | mlfromscratch/supervised_learning/neuroevolution.py | .py | from __future__ import print_function, division
import numpy as np
import copy
class Neuroevolution():
""" Evolutionary optimization of Neural Networks.
Parameters:
-----------
n_individuals: int
The number of neural networks that are allowed in the population at a time.
mutation_rate: flo... | 127 | 6,006 |
ML-From-Scratch | mlfromscratch/supervised_learning/perceptron.py | .py | from __future__ import print_function, division
import math
import numpy as np
# Import helper functions
from mlfromscratch.utils import train_test_split, to_categorical, normalize, accuracy_score
from mlfromscratch.deep_learning.activation_functions import Sigmoid, ReLU, SoftPlus, LeakyReLU, TanH, ELU
from mlfromscra... | 62 | 2,679 |
ML-From-Scratch | mlfromscratch/supervised_learning/decision_tree.py | .py | from __future__ import division, print_function
import numpy as np
from mlfromscratch.utils import divide_on_feature, train_test_split, standardize, mean_squared_error
from mlfromscratch.utils import calculate_entropy, accuracy_score, calculate_variance
class DecisionNode():
"""Class that represents a decision no... | 282 | 11,134 |
ML-From-Scratch | mlfromscratch/supervised_learning/multilayer_perceptron.py | .py | from __future__ import print_function, division
import numpy as np
import math
from sklearn import datasets
from mlfromscratch.utils import train_test_split, to_categorical, normalize, accuracy_score, Plot
from mlfromscratch.deep_learning.activation_functions import Sigmoid, Softmax
from mlfromscratch.deep_learning.lo... | 118 | 4,390 |
ML-From-Scratch | mlfromscratch/supervised_learning/k_nearest_neighbors.py | .py | from __future__ import print_function, division
import numpy as np
from mlfromscratch.utils import euclidean_distance
class KNN():
""" K Nearest Neighbors classifier.
Parameters:
-----------
k: int
The number of closest neighbors that will determine the class of the
sample that we wis... | 34 | 1,265 |
ML-From-Scratch | mlfromscratch/examples/bayesian_regression.py | .py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Import helper functions
from mlfromscratch.utils.data_operation import mean_squared_error
from mlfromscratch.utils.data_manipulation import train_test_split, polynomial_features
from mlfromscratch.supervised_learning import BayesianRegression
de... | 79 | 2,472 |
ML-From-Scratch | mlfromscratch/examples/gradient_boosting_regressor.py | .py | from __future__ import division, print_function
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import progressbar
from mlfromscratch.utils import train_test_split, standardize, to_categorical
from mlfromscratch.utils import mean_squared_error, accuracy_score, Plot
from mlfromscratch.utils.loss_... | 55 | 1,849 |
ML-From-Scratch | mlfromscratch/examples/naive_bayes.py | .py | from __future__ import division, print_function
from sklearn import datasets
import numpy as np
from mlfromscratch.utils import train_test_split, normalize, accuracy_score, Plot
from mlfromscratch.supervised_learning import NaiveBayes
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = d... | 26 | 789 |
ML-From-Scratch | mlfromscratch/examples/restricted_boltzmann_machine.py | .py | import logging
import numpy as np
from sklearn import datasets
from sklearn.datasets import fetch_mldata
import matplotlib.pyplot as plt
from mlfromscratch.unsupervised_learning import RBM
logging.basicConfig(level=logging.DEBUG)
def main():
mnist = fetch_mldata('MNIST original')
X = mnist.data / 255.0
... | 66 | 1,821 |
ML-From-Scratch | mlfromscratch/examples/decision_tree_classifier.py | .py | from __future__ import division, print_function
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
import sys
import os
# Import helper functions
from mlfromscratch.utils import train_test_split, standardize, accuracy_score
from mlfromscratch.utils import mean_squared_error, calculate_vari... | 38 | 956 |
ML-From-Scratch | mlfromscratch/examples/gaussian_mixture_model.py | .py | from __future__ import division, print_function
import sys
import os
import math
import random
from sklearn import datasets
import numpy as np
from mlfromscratch.unsupervised_learning import GaussianMixtureModel
from mlfromscratch.utils import Plot
def main():
# Load the dataset
X, y = datasets.make_blobs()
... | 27 | 565 |
ML-From-Scratch | mlfromscratch/examples/partitioning_around_medoids.py | .py | from sklearn import datasets
import numpy as np
# Import helper functions
from mlfromscratch.utils import Plot
from mlfromscratch.unsupervised_learning import PAM
def main():
# Load the dataset
X, y = datasets.make_blobs()
# Cluster the data using K-Medoids
clf = PAM(k=3)
y_pred = clf.predict(X)
... | 23 | 540 |
ML-From-Scratch | mlfromscratch/examples/fp_growth.py | .py |
import numpy as np
from mlfromscratch.unsupervised_learning import FPGrowth
def main():
# Demo transaction set
# Example:
# https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm
transactions = np.array([
["A", "B", "D", "E"],
["B... | 44 | 1,119 |
ML-From-Scratch | mlfromscratch/examples/multi_class_lda.py | .py | from __future__ import print_function
from sklearn import datasets
import numpy as np
from mlfromscratch.supervised_learning import MultiClassLDA
from mlfromscratch.utils import normalize
def main():
# Load the dataset
data = datasets.load_iris()
X = normalize(data.data)
y = data.target
# Project... | 19 | 486 |
ML-From-Scratch | mlfromscratch/examples/particle_swarm_optimization.py | .py |
from __future__ import print_function
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
from mlfromscratch.supervised_learning import ParticleSwarmOptimizedNN
from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
from mlfromscratch.deep_learning import NeuralN... | 73 | 2,569 |
ML-From-Scratch | mlfromscratch/examples/genetic_algorithm.py | .py |
from mlfromscratch.unsupervised_learning import GeneticAlgorithm
def main():
target_string = "Genetic Algorithm"
population_size = 100
mutation_rate = 0.05
genetic_algorithm = GeneticAlgorithm(target_string,
population_size,
... | 34 | 1,333 |
ML-From-Scratch | mlfromscratch/examples/xgboost.py | .py | from __future__ import division, print_function
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
import progressbar
from mlfromscratch.utils import train_test_split, standardize, to_categorical, normalize
from mlfromscratch.utils import mean_squared_error, accuracy_score, Plot
from mlfrom... | 36 | 897 |
ML-From-Scratch | mlfromscratch/examples/recurrent_neural_network.py | .py | from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
from mlfromscratch.deep_learning import NeuralNetwork
from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
from mlfromscratch.utils import get_random_subsets, shuffle_data, accuracy_score
from mlfromsc... | 89 | 3,225 |
ML-From-Scratch | mlfromscratch/examples/linear_discriminant_analysis.py | .py | from __future__ import print_function
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
from mlfromscratch.supervised_learning import LDA
from mlfromscratch.utils import calculate_covariance_matrix, accuracy_score
from mlfromscratch.utils import normalize, standardize, train_test_split, P... | 36 | 929 |
ML-From-Scratch | mlfromscratch/examples/support_vector_machine.py | .py | from __future__ import division, print_function
import numpy as np
from sklearn import datasets
# Import helper functions
from mlfromscratch.utils import train_test_split, normalize, accuracy_score, Plot
from mlfromscratch.utils.kernels import *
from mlfromscratch.supervised_learning import SupportVectorMachine
def m... | 31 | 969 |
ML-From-Scratch | mlfromscratch/examples/principal_component_analysis.py | .py | from sklearn import datasets
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
import matplotlib.colors as colors
import numpy as np
from mlfromscratch.unsupervised_learning import PCA
def main():
# Demo of how to reduce the dimensionality of the data to two dimension
# and plot the results.
#... | 47 | 1,201 |
ML-From-Scratch | mlfromscratch/examples/random_forest.py | .py | from __future__ import division, print_function
import numpy as np
from sklearn import datasets
from mlfromscratch.utils import train_test_split, accuracy_score, Plot
from mlfromscratch.supervised_learning import RandomForest
def main():
data = datasets.load_digits()
X = data.data
y = data.target
X_tr... | 27 | 738 |
ML-From-Scratch | mlfromscratch/examples/gradient_boosting_classifier.py | .py | from __future__ import division, print_function
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
# Import helper functions
from mlfromscratch.utils import train_test_split, accuracy_score
from mlfromscratch.deep_learning.loss_functions import CrossEntropy
from mlfromscratch.utils import ... | 39 | 985 |
ML-From-Scratch | mlfromscratch/examples/ridge_regression.py | .py | from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Import helper functions
from mlfromscratch.supervised_learning import PolynomialRidgeRegression
from mlfromscratch.utils import k_fold_cross_validation_sets, normalize, Plot
from mlfromscratch.utils import tra... | 83 | 2,967 |
ML-From-Scratch | mlfromscratch/examples/adaboost.py | .py | from __future__ import division, print_function
import numpy as np
from sklearn import datasets
# Import helper functions
from mlfromscratch.supervised_learning import Adaboost
from mlfromscratch.utils.data_manipulation import train_test_split
from mlfromscratch.utils.data_operation import accuracy_score
from mlfromsc... | 40 | 1,139 |
ML-From-Scratch | mlfromscratch/examples/dbscan.py | .py | import sys
import os
import math
import random
from sklearn import datasets
import numpy as np
# Import helper functions
from mlfromscratch.utils import Plot
from mlfromscratch.unsupervised_learning import DBSCAN
def main():
# Load the dataset
X, y = datasets.make_moons(n_samples=300, noise=0.08, shuffle=Fals... | 27 | 642 |
ML-From-Scratch | mlfromscratch/examples/lasso_regression.py | .py | from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Import helper functions
from mlfromscratch.supervised_learning import LassoRegression
from mlfromscratch.utils import k_fold_cross_validation_sets, normalize, mean_squared_error
from mlfromscratch.utils import... | 63 | 1,988 |
ML-From-Scratch | mlfromscratch/examples/demo.py | .py | from __future__ import print_function
from sklearn import datasets
import numpy as np
import math
import matplotlib.pyplot as plt
from mlfromscratch.utils import train_test_split, normalize, to_categorical, accuracy_score
from mlfromscratch.deep_learning.optimizers import Adam
from mlfromscratch.deep_learning.loss_fun... | 148 | 4,677 |
ML-From-Scratch | mlfromscratch/examples/logistic_regression.py | .py | from __future__ import print_function
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
# Import helper functions
from mlfromscratch.utils import make_diagonal, normalize, train_test_split, accuracy_score
from mlfromscratch.deep_learning.activation_functions import Sigmoid
from mlfromscra... | 33 | 1,062 |
ML-From-Scratch | mlfromscratch/examples/apriori.py | .py | from __future__ import division, print_function
import numpy as np
from mlfromscratch.unsupervised_learning import Apriori
def main():
# Demo transaction set
# Example 2: https://en.wikipedia.org/wiki/Apriori_algorithm
transactions = np.array([[1, 2, 3, 4], [1, 2, 4], [1, 2], [2, 3, 4], [2, 3], [3, 4], [2... | 35 | 1,178 |
ML-From-Scratch | mlfromscratch/examples/linear_regression.py | .py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_regression
from mlfromscratch.utils import train_test_split, polynomial_features
from mlfromscratch.utils import mean_squared_error, Plot
from mlfromscratch.supervised_learning import LinearRegression
def main():
... | 52 | 1,610 |
ML-From-Scratch | mlfromscratch/examples/elastic_net.py | .py | from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Import helper functions
from mlfromscratch.supervised_learning import ElasticNet
from mlfromscratch.utils import k_fold_cross_validation_sets, normalize, mean_squared_error
from mlfromscratch.utils import trai... | 64 | 1,999 |
ML-From-Scratch | mlfromscratch/examples/deep_q_network.py | .py | from __future__ import print_function
import numpy as np
from mlfromscratch.utils import to_categorical
from mlfromscratch.deep_learning.optimizers import Adam
from mlfromscratch.deep_learning.loss_functions import SquareLoss
from mlfromscratch.deep_learning.layers import Dense, Dropout, Flatten, Activation, Reshape, B... | 35 | 1,117 |
ML-From-Scratch | mlfromscratch/examples/decision_tree_regressor.py | .py | from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from mlfromscratch.utils import train_test_split, standardize, accuracy_score
from mlfromscratch.utils import mean_squared_error, calculate_variance, Plot
from mlfromscratch.supervised_learning import... | 52 | 1,599 |
ML-From-Scratch | mlfromscratch/examples/k_means.py | .py | from __future__ import division, print_function
from sklearn import datasets
import numpy as np
from mlfromscratch.unsupervised_learning import KMeans
from mlfromscratch.utils import Plot
def main():
# Load the dataset
X, y = datasets.make_blobs()
# Cluster the data using K-Means
clf = KMeans(k=3)
... | 25 | 572 |
ML-From-Scratch | mlfromscratch/examples/neuroevolution.py | .py |
from __future__ import print_function
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
from mlfromscratch.supervised_learning import Neuroevolution
from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
from mlfromscratch.deep_learning import NeuralNetwork
fro... | 62 | 2,167 |
ML-From-Scratch | mlfromscratch/examples/perceptron.py | .py | from __future__ import print_function
from sklearn import datasets
import numpy as np
# Import helper functions
from mlfromscratch.utils import train_test_split, normalize, to_categorical, accuracy_score
from mlfromscratch.deep_learning.activation_functions import Sigmoid
from mlfromscratch.deep_learning.loss_function... | 42 | 1,253 |
ML-From-Scratch | mlfromscratch/examples/convolutional_neural_network.py | .py |
from __future__ import print_function
from sklearn import datasets
import matplotlib.pyplot as plt
import math
import numpy as np
# Import helper functions
from mlfromscratch.deep_learning import NeuralNetwork
from mlfromscratch.utils import train_test_split, to_categorical, normalize
from mlfromscratch.utils import ... | 88 | 2,904 |
ML-From-Scratch | mlfromscratch/examples/polynomial_regression.py | .py | from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Import helper functions
from mlfromscratch.supervised_learning import PolynomialRidgeRegression
from mlfromscratch.utils import k_fold_cross_validation_sets, normalize, mean_squared_error
from mlfromscratch.ut... | 82 | 2,985 |
ML-From-Scratch | mlfromscratch/examples/multilayer_perceptron.py | .py |
from __future__ import print_function
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
# Import helper functions
from mlfromscratch.deep_learning import NeuralNetwork
from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
from mlfromscratch.utils import get_ra... | 79 | 2,441 |
ML-From-Scratch | mlfromscratch/examples/k_nearest_neighbors.py | .py | from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from mlfromscratch.utils import train_test_split, normalize, accuracy_score
from mlfromscratch.utils import euclidean_distance, Plot
from mlfromscratch.supervised_learning import KNN
def main():
d... | 28 | 851 |
ML-From-Scratch | mlfromscratch/deep_learning/neural_network.py | .py | from __future__ import print_function, division
from terminaltables import AsciiTable
import numpy as np
import progressbar
from mlfromscratch.utils import batch_iterator
from mlfromscratch.utils.misc import bar_widgets
class NeuralNetwork():
"""Neural Network. Deep Learning base model.
Parameters:
-----... | 124 | 4,750 |
ML-From-Scratch | mlfromscratch/deep_learning/loss_functions.py | .py | from __future__ import division
import numpy as np
from mlfromscratch.utils import accuracy_score
from mlfromscratch.deep_learning.activation_functions import Sigmoid
class Loss(object):
def loss(self, y_true, y_pred):
return NotImplementedError()
def gradient(self, y, y_pred):
raise NotImplem... | 42 | 1,045 |
ML-From-Scratch | mlfromscratch/deep_learning/activation_functions.py | .py | import numpy as np
# Collection of activation functions
# Reference: https://en.wikipedia.org/wiki/Activation_function
class Sigmoid():
def __call__(self, x):
return 1 / (1 + np.exp(-x))
def gradient(self, x):
return self.__call__(x) * (1 - self.__call__(x))
class Softmax():
def __call__... | 76 | 1,992 |
ML-From-Scratch | mlfromscratch/deep_learning/layers.py | .py |
from __future__ import print_function, division
import math
import numpy as np
import copy
from mlfromscratch.deep_learning.activation_functions import Sigmoid, ReLU, SoftPlus, LeakyReLU
from mlfromscratch.deep_learning.activation_functions import TanH, ELU, SELU, Softmax
class Layer(object):
def set_input_shap... | 734 | 27,518 |
ML-From-Scratch | mlfromscratch/deep_learning/optimizers.py | .py | import numpy as np
from mlfromscratch.utils import make_diagonal, normalize
# Optimizers for models that use gradient based methods for finding the
# weights that minimizes the loss.
# A great resource for understanding these methods:
# http://sebastianruder.com/optimizing-gradient-descent/index.html
class Stochast... | 134 | 4,774 |
pytorch-tutorial | tutorials/04-utils/tensorboard/logger.py | .py | # Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import tensorflow as tf
import numpy as np
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO # Python 3.x
class Logger(object):
def __init__(self... | 71 | 2,466 |
pytorch-tutorial | tutorials/04-utils/tensorboard/main.py | .py | import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from logger import Logger
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# MNIST dataset
dataset = torchvision.datasets.MNIST(root='../../data',
... | 97 | 3,161 |
pytorch-tutorial | tutorials/01-basics/logistic_regression/main.py | .py | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Hyper-parameters
input_size = 28 * 28 # 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001
# MNIST dataset (images and labels)
train_dataset = torchvision.datasets.MNIST(root='../../data', ... | 77 | 2,578 |
pytorch-tutorial | tutorials/01-basics/feedforward_neural_network/main.py | .py | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001
... | 94 | 3,136 |
pytorch-tutorial | tutorials/01-basics/linear_regression/main.py | .py | import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
# Hyper-parameters
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001
# Toy dataset
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
[9.779], [6.182], [7.59], [2.167], [7.042]... | 55 | 1,553 |
pytorch-tutorial | tutorials/01-basics/pytorch_basics/main.py | .py | import torch
import torchvision
import torch.nn as nn
import numpy as np
import torchvision.transforms as transforms
# ================================================================== #
# Table of Contents #
# ========================================================... | 190 | 6,311 |
pytorch-tutorial | tutorials/02-intermediate/convolutional_neural_network/main.py | .py | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Hyper parameters
num_epochs = 5
num_classes = 10
batch_size = 100
learning_rate = 0.001
# MNIST dataset
train_dataset = ... | 100 | 3,362 |
pytorch-tutorial | tutorials/02-intermediate/recurrent_neural_network/main.py | .py | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
sequence_length = 28
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
nu... | 103 | 3,536 |
pytorch-tutorial | tutorials/02-intermediate/deep_residual_network/main.py | .py | # ---------------------------------------------------------------------------- #
# An implementation of https://arxiv.org/pdf/1512.03385.pdf #
# See section 4.2 for the model architecture on CIFAR-10 #
# Some part of the code was referenced from below ... | 171 | 5,870 |
pytorch-tutorial | tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py | .py | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
sequence_length = 28
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
nu... | 102 | 3,600 |
pytorch-tutorial | tutorials/02-intermediate/language_model/main.py | .py | # Some part of the code was referenced from below.
# https://github.com/pytorch/examples/tree/master/word_language_model
import torch
import torch.nn as nn
import numpy as np
from torch.nn.utils import clip_grad_norm_
from data_utils import Dictionary, Corpus
# Device configuration
device = torch.device('cuda' if to... | 120 | 4,024 |
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