diff --git a/NeuralNetworks-python/.editorconfig b/NeuralNetworks-python/.editorconfig
new file mode 100644
index 0000000..b06d4c9
--- /dev/null
+++ b/NeuralNetworks-python/.editorconfig
@@ -0,0 +1,10 @@
+root = true
+
+[*]
+
+charset = utf-8
+indent_style = space
+indent_size = 2
+trim_trailing_whitespace = true
+insert_final_newline = true
+end_of_line = lf
diff --git a/NeuralNetworks-python/.gitignore b/NeuralNetworks-python/.gitignore
index 7bbc71c..832d03f 100644
--- a/NeuralNetworks-python/.gitignore
+++ b/NeuralNetworks-python/.gitignore
@@ -1,101 +1,6 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
 .eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
 *.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv
+__pycache__/
 venv/
-ENV/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
+build/
+dist/
diff --git a/NeuralNetworks-python/Adaline.py b/NeuralNetworks-python/Adaline.py
deleted file mode 100755
index 6d58ef8..0000000
--- a/NeuralNetworks-python/Adaline.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import numpy as np
-from _data import DataSets
-from _math import ActivationFunctions, MathUtils
-from _plot import PlotUtils
-
-class Adaline:
-
-    def __init__(self, n, g, e):
-        self.n = n # learning rate
-        self.g = g # activation function
-        self.e = e # error variation tolerance
-        self.plot_data_x = [] # epochs for plotting
-        self.plot_data_y = [] # eqms for plotting
-
-    def train(self, x, d):
-        k = len(x)
-        w = np.random.rand(len(x[0]))
-        epoch = 0
-        while True:
-            mse_before = MathUtils.mean_squared_error(w, x, d)
-            for i in range(0, k):
-                v = np.dot(np.transpose(w), x[i])
-                w = np.add(w, np.multiply(x[i], self.n * (d[i] - v)))
-            epoch = epoch + 1
-            mse_after = MathUtils.mean_squared_error(w, x, d)
-            print(f"Epoch: {epoch}\tWeights: {w}\tError: {mse_after:.12f}")
-            self.plot_data_x.append(epoch)
-            self.plot_data_y.append(mse_after)
-            if abs(mse_after - mse_before) <= self.e:
-                break
-        return w
-
-    def test(self, w, x):
-        v = np.dot(np.transpose(w), x)
-        y = self.g(v)
-        return y
-    
-    def evaluate(self, w, x, d):
-        correct = 0
-        total = len(x)
-        for i in range(0, len(x)):
-            y = self.test(w, x[i])
-            if (y == d[i]):
-                correct = correct + 1
-        accuracy = 100.0 * (float(correct) / float(total))
-        print(f"Accuracy: {accuracy:.2f}% ({correct}/{total})")
-        return accuracy
-
-if __name__ == "__main__":
-
-    # set random number generator seed
-    np.random.seed(NUMERO_DE_MATRICULA)
-
-    # set floating point formatting when printing
-    np.set_printoptions(formatter={"float": "{: 0.6f}".format})
-
-    # load data
-    x = DataSets.NOME_DO_DATASET.input
-    d = DataSets.NOME_DO_DATASET.output
-
-    # define the network parameters
-    n = TAXA_DE_APRENDIZADO
-    g = ActivationFunctions.FUNCAO_DE_ATIVACAO
-    e = TOLERANCIA_DE_ERRO
-
-    # create the neural network
-    nn = Adaline(n, g, e)
-
-    # train the neural network
-    w = nn.train(x, d)
-
-    # evaluate the neural network
-    acc = nn.evaluate(w, x, d)
-    
-    # plot epoch versus error data
-    PlotUtils.plot(nn.plot_data_x, "epoch", nn.plot_data_y, "mse")
diff --git a/NeuralNetworks-python/LICENSE.md b/NeuralNetworks-python/LICENSE.md
new file mode 100644
index 0000000..8c319f6
--- /dev/null
+++ b/NeuralNetworks-python/LICENSE.md
@@ -0,0 +1,18 @@
+Copyright (c) 2020, Marcelo Cysneiros and Danilo Peixoto. All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in the
+Software without restriction, including without limitation the rights to use,
+copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN
+AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/NeuralNetworks-python/Perceptron.py b/NeuralNetworks-python/Perceptron.py
deleted file mode 100755
index f744a90..0000000
--- a/NeuralNetworks-python/Perceptron.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import numpy as np
-from _data import DataSets
-from _math import ActivationFunctions
-from _plot import PlotUtils
-
-class Perceptron:
-
-    def __init__(self, n, g):
-        self.n = n # learning rate
-        self.g = g # activation function
-        self.plot_data_x = [] # epochs for plotting
-        self.plot_data_y = [] # error for plotting
-
-    def train(self, x, d):
-        k = len(x)
-        w = np.random.rand(len(x[0]))
-        epoch = 0
-        error = True
-        while error and epoch < 10000:
-            error = False
-            for i in range(0, k):
-                v = np.dot(np.transpose(w), x[i])
-                y = self.g(v)
-                if y != d[i]:
-                    w = np.add(w, np.multiply(self.n * (d[i] - y), x[i]))
-                    error = True
-            epoch = epoch + 1
-            print(f"Epoch: {epoch}\tWeights: {w}")
-            self.plot_data_x.append(epoch)
-            self.plot_data_y.append(1 if error else 0)
-        return w
-
-    def test(self, w, x):
-        v = np.dot(np.transpose(w), x)
-        y = self.g(v)
-        return y
-    
-    def evaluate(self, w, x, d):
-        correct = 0
-        total = len(x)
-        for i in range(0, len(x)):
-            y = self.test(w, x[i])
-            if (y == d[i]):
-                correct = correct + 1
-        accuracy = 100.0 * (float(correct) / float(total))
-        print(f"Accuracy: {accuracy:.2f}% ({correct}/{total})")
-        return accuracy
-
-if __name__ == "__main__":
-
-    # set random number generator seed
-    np.random.seed(NUMERO_DE_MATRICULA)
-
-    # set floating point formatting when printing
-    np.set_printoptions(formatter={"float": "{: 0.6f}".format})
-
-    # load data
-    x = DataSets.NOME_DO_DATASET.input
-    d = DataSets.NOME_DO_DATASET.output
-
-    # define the network parameters
-    n = TAXA_DE_APRENDIZADO
-    g = ActivationFunctions.FUNCAO_DE_ATIVACAO
-
-    # create the neural network
-    nn = Perceptron(n, g)
-
-    # train the neural network
-    w = nn.train(x, d)
-
-    # evaluate the neural network
-    acc = nn.evaluate(w, x, d)
-    
-    # plot epoch versus error data
-    PlotUtils.plot(nn.plot_data_x, "epoch", nn.plot_data_y, "error")
diff --git a/NeuralNetworks-python/README.md b/NeuralNetworks-python/README.md
new file mode 100644
index 0000000..d1e7454
--- /dev/null
+++ b/NeuralNetworks-python/README.md
@@ -0,0 +1,33 @@
+# Blueprint
+
+A simple neural network package.
+
+## Installation
+
+Prerequisites:
+
+* [Python >=3.6.0](https://www.python.org)
+
+Install package:
+
+```
+pip install .
+```
+
+Use the `-e, --editable` flag to install package in development mode.
+
+## Usage
+
+Check the sample scripts available in the `scripts` directory.
+
+Run sample scripts:
+
+```
+python scripts/perceptron.py
+```
+
+## Copyright and license
+
+Copyright (c) 2020, Marcelo Cysneiros and Danilo Peixoto. All rights reserved.
+
+Project developed under a [MIT License](LICENSE.md).
diff --git a/NeuralNetworks-python/_data/DataSets.py b/NeuralNetworks-python/_data/DataSets.py
deleted file mode 100755
index ff2dc4a..0000000
--- a/NeuralNetworks-python/_data/DataSets.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import numpy as np
-import os, sys
-from numpy.random import sample
-from numpy import append, genfromtxt
-
-class DataSets:
-    
-    @staticmethod
-    def read(folder, filename, flatten):
-        filename_abs = os.path.join(os.path.dirname(__file__), folder, filename)
-        return genfromtxt(filename_abs, delimiter=',', dtype=float)
-
-    @staticmethod
-    def add_bias(arr, bias = -1):
-        biased_arr = np.ndarray(shape=(arr.shape[0], arr.shape[1]+1), dtype=float)
-        for i in range(0, len(arr)):
-            biased_arr[i] = np.append(bias, arr[i])
-        return biased_arr
-
-# https://en.wikipedia.org/wiki/AND_gate
-class LOGIC_GATE_AND:
-    input = DataSets.add_bias(DataSets.read('logic-gate-and', 'input.txt', False))
-    output = DataSets.read('logic-gate-and', 'output.txt', True)
-
-# https://en.wikipedia.org/wiki/OR_gate
-class LOGIC_GATE_OR:
-    input = DataSets.add_bias(DataSets.read('logic-gate-or', 'input.txt', False))
-    output = DataSets.read('logic-gate-or', 'output.txt', True)
-
-# https://en.wikipedia.org/wiki/XOR_gate
-class LOGIC_GATE_XOR:
-    input = DataSets.add_bias(DataSets.read('logic-gate-xor', 'input.txt', False))
-    output = DataSets.read('logic-gate-xor', 'output.txt', True)
-
-# http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center
-class BLOOD_TRANSFUSION:
-    input = DataSets.add_bias(DataSets.read('blood-transfusion', 'input.txt', False))
-    output = DataSets.read('blood-transfusion', 'output.txt', True)
-
-# http://archive.ics.uci.edu/ml/datasets/Cryotherapy+Dataset+
-class CRYOTHERAPY:
-    input = DataSets.add_bias(DataSets.read('cryotherapy', 'input.txt', False))
-    output = DataSets.read('cryotherapy', 'output.txt', True)
-
-# https://www.kaggle.com/kumargh/pimaindiansdiabetescsv
-class DIABETES:
-    input = DataSets.add_bias(DataSets.read('diabetes', 'input.txt', False))
-    output = DataSets.read('diabetes', 'output.txt', True)
-
-# https://archive.ics.uci.edu/ml/datasets/Tic-Tac-Toe+Endgame
-class TIC_TAC_TOE_ENDGAME:
-    input = DataSets.add_bias(DataSets.read('tic-tac-toe-endgame', 'input.txt', False))
-    output = DataSets.read('tic-tac-toe-endgame', 'output.txt', True)
diff --git a/NeuralNetworks-python/_math/ActivationFunctions.py b/NeuralNetworks-python/_math/ActivationFunctions.py
deleted file mode 100755
index 47053dd..0000000
--- a/NeuralNetworks-python/_math/ActivationFunctions.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import numpy as np
-import random as rnd
-
-class ActivationFunctions:
-    
-    def __init__(self):
-        pass
-
-def heaviside(v):
-    return 1 if v >= 0 else 0
-
-def heaviside_symmetric(v):
-    return np.sign(v)
diff --git a/NeuralNetworks-python/_math/MathUtils.py b/NeuralNetworks-python/_math/MathUtils.py
deleted file mode 100644
index eadba89..0000000
--- a/NeuralNetworks-python/_math/MathUtils.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import numpy as np
-import random as rnd
-
-class MathUtils:
-    
-    def __init__(self):
-        pass
-
-def mean_squared_error(w, x, d):
-    mse = 0
-    for i in range(0,len(x)):
-        v = np.dot(np.transpose(w), x[i])
-        mse = mse + pow(d[i] - v, 2)
-    mse = mse / len(x)
-    return mse
diff --git a/NeuralNetworks-python/_math/__init__.py b/NeuralNetworks-python/_math/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/NeuralNetworks-python/_plot/PlotUtils.py b/NeuralNetworks-python/_plot/PlotUtils.py
deleted file mode 100644
index 738caa1..0000000
--- a/NeuralNetworks-python/_plot/PlotUtils.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import matplotlib.pyplot as plt
-from matplotlib.ticker import MaxNLocator
-import datetime as dt
-import numpy as np
-
-class PlotUtils:
-    
-    def __init__(self):
-        pass
-    
-# https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes
-def plot(x, _xlabel, y, _ylabel):
-    
-    # handle convergence in first training epoch
-    if len(x) == 1 and len(y) == 1:
-        print('Only one training epoch was performed; no plot to show.')
-    else:
-        # data
-        ax = plt.gca()
-        ax.plot(x, y, color='blue', linewidth=1.5)
-        ax.xaxis.set_major_locator(MaxNLocator(integer=True))
-        
-        # limits
-        ax.set_xlim([np.min(x), np.max(x)])
-        ax.set_ylim([np.min(y), np.max(y)])
-        
-        # text
-        ax.set_xlabel(_xlabel)
-        ax.set_ylabel(_ylabel)
-        ax.set_title('{} vs {}'.format(_xlabel, _ylabel))
-        
-        # display
-        ax.grid()
-        plt.show()
diff --git a/NeuralNetworks-python/_plot/__init__.py b/NeuralNetworks-python/_plot/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/NeuralNetworks-python/blueprint/__init__.py b/NeuralNetworks-python/blueprint/__init__.py
new file mode 100644
index 0000000..6495c7b
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/__init__.py
@@ -0,0 +1 @@
+from .package import *
diff --git a/NeuralNetworks-python/blueprint/activation_functions.py b/NeuralNetworks-python/blueprint/activation_functions.py
new file mode 100644
index 0000000..c50695c
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/activation_functions.py
@@ -0,0 +1,14 @@
+import numpy as np
+
+
+def linear(x):
+  return x
+
+def gaussian(x, mu, sigma):
+  return np.exp(-np.divide(np.square(x - mu), 2 * np.square(sigma)))
+
+def heaviside(x):
+  return 1 if x >= 0 else 0
+
+def heaviside_symmetric(x):
+  return np.sign(x)
diff --git a/NeuralNetworks-python/_data/__init__.py b/NeuralNetworks-python/blueprint/data/__init__.py
similarity index 100%
rename from NeuralNetworks-python/_data/__init__.py
rename to NeuralNetworks-python/blueprint/data/__init__.py
diff --git a/NeuralNetworks-python/_data/blood-transfusion/blood-transfusion.csv b/NeuralNetworks-python/blueprint/data/blood_transfusion/blood-transfusion.csv
similarity index 100%
rename from NeuralNetworks-python/_data/blood-transfusion/blood-transfusion.csv
rename to NeuralNetworks-python/blueprint/data/blood_transfusion/blood-transfusion.csv
diff --git a/NeuralNetworks-python/_data/blood-transfusion/blood-transfusion.png b/NeuralNetworks-python/blueprint/data/blood_transfusion/blood-transfusion.png
similarity index 100%
rename from NeuralNetworks-python/_data/blood-transfusion/blood-transfusion.png
rename to NeuralNetworks-python/blueprint/data/blood_transfusion/blood-transfusion.png
diff --git a/NeuralNetworks-python/_data/blood-transfusion/input.txt b/NeuralNetworks-python/blueprint/data/blood_transfusion/input.txt
similarity index 100%
rename from NeuralNetworks-python/_data/blood-transfusion/input.txt
rename to NeuralNetworks-python/blueprint/data/blood_transfusion/input.txt
diff --git a/NeuralNetworks-python/_data/blood-transfusion/output.txt b/NeuralNetworks-python/blueprint/data/blood_transfusion/output.txt
similarity index 100%
rename from NeuralNetworks-python/_data/blood-transfusion/output.txt
rename to NeuralNetworks-python/blueprint/data/blood_transfusion/output.txt
diff --git a/NeuralNetworks-python/_data/cryotherapy/input.txt b/NeuralNetworks-python/blueprint/data/cryotherapy/input.txt
similarity index 100%
rename from NeuralNetworks-python/_data/cryotherapy/input.txt
rename to NeuralNetworks-python/blueprint/data/cryotherapy/input.txt
diff --git a/NeuralNetworks-python/_data/cryotherapy/output.txt b/NeuralNetworks-python/blueprint/data/cryotherapy/output.txt
similarity index 100%
rename from NeuralNetworks-python/_data/cryotherapy/output.txt
rename to NeuralNetworks-python/blueprint/data/cryotherapy/output.txt
diff --git a/NeuralNetworks-python/_data/diabetes/diabetes.csv b/NeuralNetworks-python/blueprint/data/diabetes/diabetes.csv
similarity index 100%
rename from NeuralNetworks-python/_data/diabetes/diabetes.csv
rename to NeuralNetworks-python/blueprint/data/diabetes/diabetes.csv
diff --git a/NeuralNetworks-python/_data/diabetes/diabetes.png b/NeuralNetworks-python/blueprint/data/diabetes/diabetes.png
similarity index 100%
rename from NeuralNetworks-python/_data/diabetes/diabetes.png
rename to NeuralNetworks-python/blueprint/data/diabetes/diabetes.png
diff --git a/NeuralNetworks-python/_data/diabetes/input.txt b/NeuralNetworks-python/blueprint/data/diabetes/input.txt
similarity index 100%
rename from NeuralNetworks-python/_data/diabetes/input.txt
rename to NeuralNetworks-python/blueprint/data/diabetes/input.txt
diff --git a/NeuralNetworks-python/_data/diabetes/output.txt b/NeuralNetworks-python/blueprint/data/diabetes/output.txt
similarity index 100%
rename from NeuralNetworks-python/_data/diabetes/output.txt
rename to NeuralNetworks-python/blueprint/data/diabetes/output.txt
diff --git a/NeuralNetworks-python/_data/logic-gate-and/input.txt b/NeuralNetworks-python/blueprint/data/logic_gate_and/input.txt
old mode 100755
new mode 100644
similarity index 100%
rename from NeuralNetworks-python/_data/logic-gate-and/input.txt
rename to NeuralNetworks-python/blueprint/data/logic_gate_and/input.txt
diff --git a/NeuralNetworks-python/_data/logic-gate-and/output.txt b/NeuralNetworks-python/blueprint/data/logic_gate_and/output.txt
old mode 100755
new mode 100644
similarity index 100%
rename from NeuralNetworks-python/_data/logic-gate-and/output.txt
rename to NeuralNetworks-python/blueprint/data/logic_gate_and/output.txt
diff --git a/NeuralNetworks-python/_data/logic-gate-or/input.txt b/NeuralNetworks-python/blueprint/data/logic_gate_or/input.txt
old mode 100755
new mode 100644
similarity index 100%
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rename to NeuralNetworks-python/blueprint/data/logic_gate_or/input.txt
diff --git a/NeuralNetworks-python/_data/logic-gate-or/output.txt b/NeuralNetworks-python/blueprint/data/logic_gate_or/output.txt
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similarity index 100%
rename from NeuralNetworks-python/_data/logic-gate-or/output.txt
rename to NeuralNetworks-python/blueprint/data/logic_gate_or/output.txt
diff --git a/NeuralNetworks-python/_data/logic-gate-xor/input.txt b/NeuralNetworks-python/blueprint/data/logic_gate_xor/input.txt
old mode 100755
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similarity index 100%
rename from NeuralNetworks-python/_data/logic-gate-xor/input.txt
rename to NeuralNetworks-python/blueprint/data/logic_gate_xor/input.txt
diff --git a/NeuralNetworks-python/_data/logic-gate-xor/output.txt b/NeuralNetworks-python/blueprint/data/logic_gate_xor/output.txt
old mode 100755
new mode 100644
similarity index 100%
rename from NeuralNetworks-python/_data/logic-gate-xor/output.txt
rename to NeuralNetworks-python/blueprint/data/logic_gate_xor/output.txt
diff --git a/NeuralNetworks-python/_data/tic-tac-toe-endgame/input.txt b/NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/input.txt
similarity index 100%
rename from NeuralNetworks-python/_data/tic-tac-toe-endgame/input.txt
rename to NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/input.txt
diff --git a/NeuralNetworks-python/_data/tic-tac-toe-endgame/output.txt b/NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/output.txt
similarity index 100%
rename from NeuralNetworks-python/_data/tic-tac-toe-endgame/output.txt
rename to NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/output.txt
diff --git a/NeuralNetworks-python/_data/tic-tac-toe-endgame/tic-tac-toe-endgame.csv b/NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/tic-tac-toe-endgame.csv
similarity index 100%
rename from NeuralNetworks-python/_data/tic-tac-toe-endgame/tic-tac-toe-endgame.csv
rename to NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/tic-tac-toe-endgame.csv
diff --git a/NeuralNetworks-python/_data/tic-tac-toe-endgame/tic-tac-toe-engame.png b/NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/tic-tac-toe-engame.png
similarity index 100%
rename from NeuralNetworks-python/_data/tic-tac-toe-endgame/tic-tac-toe-engame.png
rename to NeuralNetworks-python/blueprint/data/tic_tac_toe_endgame/tic-tac-toe-engame.png
diff --git a/NeuralNetworks-python/blueprint/data/tsne_2d/input.txt b/NeuralNetworks-python/blueprint/data/tsne_2d/input.txt
new file mode 100644
index 0000000..672b7cf
--- /dev/null
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diff --git a/NeuralNetworks-python/blueprint/data/tsne_2d/output.txt b/NeuralNetworks-python/blueprint/data/tsne_2d/output.txt
new file mode 100644
index 0000000..759ffdc
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/data/tsne_2d/output.txt
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diff --git a/NeuralNetworks-python/blueprint/data/tsne_2d/tsne_2d.csv b/NeuralNetworks-python/blueprint/data/tsne_2d/tsne_2d.csv
new file mode 100644
index 0000000..2385c7b
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/data/tsne_2d/tsne_2d.csv
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diff --git a/NeuralNetworks-python/blueprint/data/tsne_2d/tsne_2d.png b/NeuralNetworks-python/blueprint/data/tsne_2d/tsne_2d.png
new file mode 100644
index 0000000..5ece8af
Binary files /dev/null and b/NeuralNetworks-python/blueprint/data/tsne_2d/tsne_2d.png differ
diff --git a/NeuralNetworks-python/blueprint/datasets/__init__.py b/NeuralNetworks-python/blueprint/datasets/__init__.py
new file mode 100644
index 0000000..6caf0d8
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/datasets/__init__.py
@@ -0,0 +1,14 @@
+from pathlib import Path
+
+from .dataset import Dataset
+from .. import data
+
+
+logic_gate_and = Dataset('logic_gate_and', Path(data.__file__).parent.resolve() / 'logic_gate_and', bias = -1)
+logic_gate_or = Dataset('logic_gate_or', Path(data.__file__).parent.resolve() / 'logic_gate_or', bias = -1)
+logic_gate_xor = Dataset('logic_gate_xor', Path(data.__file__).parent.resolve() / 'logic_gate_xor', bias = -1)
+blood_transfusion = Dataset('blood_transfusion', Path(data.__file__).parent.resolve() / 'blood_transfusion', bias = -1)
+cryotherapy = Dataset('cryotherapy', Path(data.__file__).parent.resolve() / 'cryotherapy', bias = -1)
+diabetes = Dataset('diabetes', Path(data.__file__).parent.resolve() / 'diabetes', bias = -1)
+tic_tac_toe_endgame = Dataset('tic_tac_toe_endgame', Path(data.__file__).parent.resolve() / 'tic_tac_toe_endgame', bias = -1)
+tsne_2d = Dataset('tsne_2d', Path(data.__file__).parent.resolve() / 'tsne_2d', output_type = int)
diff --git a/NeuralNetworks-python/blueprint/datasets/dataset.py b/NeuralNetworks-python/blueprint/datasets/dataset.py
new file mode 100644
index 0000000..b95b65a
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/datasets/dataset.py
@@ -0,0 +1,16 @@
+from . import utils
+
+
+class Dataset:
+  def __init__(self, name, path, input_type = float, output_type = float, bias = None):
+    self.name = name
+    self.path = path
+    self.input_type = input_type
+    self.output_type = output_type
+    self.bias = bias
+
+    self.input = utils.read(self.path / 'input.txt', dtype = self.input_type)
+    self.output = utils.read(self.path / 'output.txt', dtype = self.output_type)
+
+    if bias is not None:
+      utils.bias(self.input, self.bias)
diff --git a/NeuralNetworks-python/blueprint/datasets/utils.py b/NeuralNetworks-python/blueprint/datasets/utils.py
new file mode 100644
index 0000000..68d60e1
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/datasets/utils.py
@@ -0,0 +1,13 @@
+import numpy as np
+
+
+def read(filename, delimiter = ',', dtype = float):
+  return np.genfromtxt(filename, delimiter = delimiter, dtype = dtype)
+
+def bias(x, value):
+  biased_x = np.ndarray(shape = (x.shape[0], x.shape[1] + 1), dtype = x.dtype)
+
+  for i in range(len(x)):
+    biased_x[i] = np.append(value, x[i])
+
+  return biased_x
diff --git a/NeuralNetworks-python/blueprint/estimators/__init__.py b/NeuralNetworks-python/blueprint/estimators/__init__.py
new file mode 100644
index 0000000..f7b3ab4
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/estimators/__init__.py
@@ -0,0 +1,4 @@
+from .estimator import *
+from .perceptron import *
+from .adaline import *
+from .kmeans import *
diff --git a/NeuralNetworks-python/blueprint/estimators/adaline.py b/NeuralNetworks-python/blueprint/estimators/adaline.py
new file mode 100644
index 0000000..02b6275
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/estimators/adaline.py
@@ -0,0 +1,66 @@
+import numpy as np
+
+from .estimator import Estimator
+
+
+class Adaline(Estimator):
+  def __init__(self, learning_rate, activation_function, loss_function, loss_variation_tolerance):
+    super().__init__()
+
+    self.learning_rate = learning_rate
+    self.activation_function = activation_function
+    self.loss_function = loss_function
+    self.loss_variation_tolerance = loss_variation_tolerance
+
+  def train(self, x, d):
+    k = len(x)
+    w = np.random.rand(len(x[0]))
+    epoch = 0
+
+    while True:
+      mse_before = self.loss_function(x, d, w)
+
+      if epoch == 0:
+        print(f'Epoch: {epoch}\tWeights: {w}\tLoss: {mse_before:.5f}')
+
+        self.plot_data_x.append(epoch)
+        self.plot_data_y.append(mse_before)
+
+      for i in range(k):
+        v = np.dot(np.transpose(w), x[i])
+        w = np.add(w, np.multiply(x[i], self.learning_rate * (d[i] - v)))
+
+      epoch = epoch + 1
+      mse_after = self.loss_function(x, d, w)
+
+      print(f'Epoch: {epoch}\tWeights: {w}\tLoss: {mse_after:.5f}')
+
+      self.plot_data_x.append(epoch)
+      self.plot_data_y.append(mse_after)
+
+      if abs(mse_after - mse_before) <= self.loss_variation_tolerance:
+        break
+
+    return w
+
+  def predict(self, x, w):
+    v = np.dot(np.transpose(w), x)
+    y = self.activation_function(v)
+
+    return y
+
+  def evaluate(self, x, d, w):
+    total = len(x)
+    correct = 0
+
+    for i in range(len(x)):
+      y = self.predict(x[i], w)
+
+      if y == d[i]:
+        correct = correct + 1
+
+    accuracy = correct / total
+
+    print(f'Accuracy: {accuracy * 100:.5f}% ({accuracy:.5f})')
+
+    return accuracy
diff --git a/NeuralNetworks-python/blueprint/estimators/estimator.py b/NeuralNetworks-python/blueprint/estimators/estimator.py
new file mode 100644
index 0000000..4bd086d
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/estimators/estimator.py
@@ -0,0 +1,16 @@
+class Estimator:
+  def __init__(self, *args, **kwargs):
+    if type(self) is Estimator:
+      raise NotImplementedError
+
+    self.plot_data_x = []
+    self.plot_data_y = []
+
+  def train(self, *args, **kwargs):
+    raise NotImplementedError
+
+  def predict(self, *args, **kwargs):
+    raise NotImplementedError
+
+  def evaluate(self, *args, **kwargs):
+    raise NotImplementedError
diff --git a/NeuralNetworks-python/blueprint/estimators/kmeans.py b/NeuralNetworks-python/blueprint/estimators/kmeans.py
new file mode 100644
index 0000000..09279f3
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/estimators/kmeans.py
@@ -0,0 +1,54 @@
+import numpy as np
+
+from . import utils
+from .estimator import Estimator
+
+
+# https://github.com/Yangyangii/MachineLearningTutorial/blob/master/Numpy/RBF-Network-with-Kmeans-clustering.ipynb
+# https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html
+class KMeans(Estimator):
+  def __init__(self, k, maximum_iterations, epsilon):
+    self.k = k
+    self.maximum_iterations = maximum_iterations
+    self.epsilon = epsilon
+
+  def train(self, x):
+    c = utils.kmeans_plus_plus(x, self.k)
+    dist = np.zeros([self.k, x.shape[0]])
+    eps2 = self.epsilon * self.epsilon
+    it = 0
+
+    while it < self.maximum_iterations:
+      for i, centroid in enumerate(c):
+        dist[i] = np.sum(np.square(np.subtract(np.broadcast_to(centroid, x.shape), x)), axis = 1)
+
+      y = np.argmin(dist, axis = 0)
+
+      prev_c = c
+      c = np.array([np.mean(x[y == i], axis = 0) for i in range(self.k)])
+
+      if np.all(np.square(c - prev_c) < eps2):
+        break
+
+      it += 1
+
+    return c
+
+  def predict(self, x, c):
+    dist = np.zeros([self.k, x.shape[0]])
+
+    for i, centroid in enumerate(c):
+      dist[i] = np.sum(np.square(np.subtract(np.broadcast_to(centroid, x.shape), x)), axis = 1)
+
+    y = np.argmin(dist, axis = 0)
+
+    return y
+
+  def evaluate(self, x, d, c):
+    y = self.predict(x, c)
+
+    accuracy = utils.rand_index_score(y, d)
+
+    print(f'Accuracy: {accuracy * 100:.5f}% ({accuracy:.5f})')
+
+    return accuracy
diff --git a/NeuralNetworks-python/blueprint/estimators/perceptron.py b/NeuralNetworks-python/blueprint/estimators/perceptron.py
new file mode 100644
index 0000000..e4f9195
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/estimators/perceptron.py
@@ -0,0 +1,65 @@
+import numpy as np
+
+from .estimator import Estimator
+
+
+class Perceptron(Estimator):
+  def __init__(self, learning_rate, activation_function, epochs):
+    super().__init__()
+
+    self.learning_rate = learning_rate
+    self.activation_function = activation_function
+    self.epochs = epochs
+
+  def train(self, x, d):
+    k = len(x)
+    w = np.random.rand(len(x[0]))
+    epoch = 0
+    error = True
+
+    print(f'Epoch: {epoch}\tWeights: {w}')
+
+    self.plot_data_x.append(epoch)
+    self.plot_data_y.append(1 if error else 0)
+
+    while error and epoch < self.epochs:
+      error = False
+
+      for i in range(k):
+        v = np.dot(np.transpose(w), x[i])
+        y = self.activation_function(v)
+
+        if y != d[i]:
+          w = np.add(w, np.multiply(self.learning_rate * (d[i] - y), x[i]))
+          error = True
+
+      epoch = epoch + 1
+
+      print(f'Epoch: {epoch}\tWeights: {w}')
+
+      self.plot_data_x.append(epoch)
+      self.plot_data_y.append(1 if error else 0)
+
+    return w
+
+  def predict(self, x, w):
+    v = np.dot(np.transpose(w), x)
+    y = self.activation_function(v)
+
+    return y
+
+  def evaluate(self, x, d, w):
+    total = len(x)
+    correct = 0
+
+    for i in range(len(x)):
+      y = self.predict(x[i], w)
+
+      if y == d[i]:
+        correct = correct + 1
+
+    accuracy = correct / total
+
+    print(f'Accuracy: {accuracy * 100:.5f}% ({accuracy:.5f})')
+
+    return accuracy
diff --git a/NeuralNetworks-python/blueprint/estimators/utils.py b/NeuralNetworks-python/blueprint/estimators/utils.py
new file mode 100644
index 0000000..e8e9bdd
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/estimators/utils.py
@@ -0,0 +1,38 @@
+import numpy as np
+from scipy.special import comb
+
+
+# https://stats.stackexchange.com/a/157385
+def rand_index_score(y, d):
+  tp_fp = comb(np.bincount(y), 2).sum()
+  tp_fn = comb(np.bincount(d), 2).sum()
+
+  A = np.c_[(y, d)]
+
+  tp = sum(comb(np.bincount(A[A[:, 0] == i, 1]), 2).sum() for i in set(y))
+
+  fp = tp_fp - tp
+  fn = tp_fn - tp
+
+  tn = comb(len(A), 2) - tp - fp - fn
+
+  return (tp + tn) / (tp + fp + fn + tn)
+
+# https://www.kdnuggets.com/2020/06/centroid-initialization-k-means-clustering.html
+def kmeans_plus_plus(x, k):
+  c = [x[0]]
+
+  for _ in range(1, k):
+    dist = np.array([min([np.inner(c - xi, c - xi) for c in c]) for xi in x])
+    probs = dist / dist.sum()
+    cumulative_probs = probs.cumsum()
+    r = np.random.rand()
+
+    for j, p in enumerate(cumulative_probs):
+      if r < p:
+        i = j
+        break
+
+    c.append(x[i])
+
+  return np.array(c)
diff --git a/NeuralNetworks-python/blueprint/loss_functions.py b/NeuralNetworks-python/blueprint/loss_functions.py
new file mode 100644
index 0000000..b028648
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/loss_functions.py
@@ -0,0 +1,13 @@
+import numpy as np
+
+
+def mean_squared_error(x, d, w):
+  mse = 0
+
+  for i in range(0, len(x)):
+    v = np.dot(np.transpose(w), x[i])
+    mse = mse + pow(d[i] - v, 2)
+
+  mse = mse / len(x)
+
+  return mse
diff --git a/NeuralNetworks-python/blueprint/package.py b/NeuralNetworks-python/blueprint/package.py
new file mode 100644
index 0000000..46616e3
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/package.py
@@ -0,0 +1,18 @@
+__title__ = 'Blueprint'
+__version__ = '2.0.0'
+__description__ = 'A simple neural network package.'
+__author__ = 'Marcelo Cysneiros and Danilo Peixoto'
+__email__ = 'marcelovca90@inatel.br'
+__license__ = 'MIT License'
+__copyright__ = 'Copyright (c) 2020, Marcelo Cysneiros and Danilo Peixoto. All rights reserved.'
+
+
+__all__ = [
+  '__title__',
+  '__version__',
+  '__description__',
+  '__author__',
+  '__email__',
+  '__license__',
+  '__copyright__'
+]
diff --git a/NeuralNetworks-python/blueprint/plot_utils.py b/NeuralNetworks-python/blueprint/plot_utils.py
new file mode 100644
index 0000000..9f6e49e
--- /dev/null
+++ b/NeuralNetworks-python/blueprint/plot_utils.py
@@ -0,0 +1,34 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MaxNLocator
+
+
+def line(x, y, x_label = 'x', y_label = 'y', color = 'blue', title = None):
+  ax = plt.gca()
+  ax.plot(x, y, color = color, linewidth = 1.5)
+  ax.xaxis.set_major_locator(MaxNLocator(integer = True))
+
+  ax.set_xlim([np.min(x), np.max(x)])
+  ax.set_ylim([np.min(y), np.max(y)])
+
+  ax.set_xlabel(x_label)
+  ax.set_ylabel(y_label)
+  ax.set_title('{} vs {}'.format(x_label, y_label) if title is None else title)
+
+  ax.grid()
+  plt.show()
+
+def scatter(x, y, x_label = 'x', y_label = 'y', colors = None, title = None):
+  ax = plt.gca()
+  ax.scatter(x, y, c = colors, s = 10)
+  ax.xaxis.set_major_locator(MaxNLocator(integer = True))
+
+  ax.set_xlim([np.min(x), np.max(x)])
+  ax.set_ylim([np.min(y), np.max(y)])
+
+  ax.set_xlabel(x_label)
+  ax.set_ylabel(y_label)
+  ax.set_title('{} vs {}'.format(x_label, y_label) if title is None else title)
+
+  ax.grid()
+  plt.show()
diff --git a/NeuralNetworks-python/scripts/adaline.py b/NeuralNetworks-python/scripts/adaline.py
new file mode 100644
index 0000000..ae3a7f7
--- /dev/null
+++ b/NeuralNetworks-python/scripts/adaline.py
@@ -0,0 +1,35 @@
+import numpy as np
+
+from blueprint import datasets, estimators, activation_functions, loss_functions, plot_utils
+
+
+if __name__ == '__main__':
+  # set random number generator seed
+  np.random.seed(0)
+
+  # set floating point formatting when printing
+  np.set_printoptions(formatter = {'float': '{:.5f}'.format})
+
+  # load data
+  dataset = datasets.logic_gate_and
+
+  x = dataset.input
+  d = dataset.output
+
+  # define the estimator parameters
+  n = 1e-3
+  g = activation_functions.heaviside
+  l = loss_functions.mean_squared_error
+  e = 1e-6
+
+  # create the estimator
+  estimator = estimators.Adaline(n, g, l, e)
+
+  # train the estimator
+  w = estimator.train(x, d)
+
+  # evaluate the estimator
+  accuracy = estimator.evaluate(x, d, w)
+
+  # plot epoch vs loss (mse)
+  plot_utils.line(estimator.plot_data_x, estimator.plot_data_y, x_label = 'epoch', y_label = 'loss (mse)')
diff --git a/NeuralNetworks-python/scripts/kmeans.py b/NeuralNetworks-python/scripts/kmeans.py
new file mode 100644
index 0000000..12c90b2
--- /dev/null
+++ b/NeuralNetworks-python/scripts/kmeans.py
@@ -0,0 +1,35 @@
+import numpy as np
+
+from blueprint import datasets, estimators, plot_utils
+
+
+if __name__ == '__main__':
+  # set random number generator seed
+  np.random.seed(0)
+
+  # set floating point formatting when printing
+  np.set_printoptions(formatter = {'float': '{:.5f}'.format})
+
+  # load data
+  dataset = datasets.tsne_2d
+
+  x = dataset.input
+  d = dataset.output
+
+  # define the estimator parameters
+  k = 4
+  iterations = 1e3
+  eps = 1e-6
+
+  # create the estimator
+  estimator = estimators.KMeans(k, iterations, eps)
+
+  # train the estimator
+  c = estimator.train(x)
+
+  # evaluate the estimator
+  accuracy = estimator.evaluate(x, d, c)
+
+  # plot clusters
+  y = estimator.predict(x, c)
+  plot_utils.scatter(x[:, 0], x[:, 1], x_label = 'tsne-1', y_label = 'tsne-2', colors = y)
diff --git a/NeuralNetworks-python/scripts/perceptron.py b/NeuralNetworks-python/scripts/perceptron.py
new file mode 100644
index 0000000..13e52f0
--- /dev/null
+++ b/NeuralNetworks-python/scripts/perceptron.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+from blueprint import datasets, estimators, activation_functions, loss_functions, plot_utils
+
+
+if __name__ == '__main__':
+  # set random number generator seed
+  np.random.seed(0)
+
+  # set floating point formatting when printing
+  np.set_printoptions(formatter = {'float': '{:.5f}'.format})
+
+  # load data
+  dataset = datasets.logic_gate_and
+
+  x = dataset.input
+  d = dataset.output
+
+  # define the estimator parameters
+  n = 1e-3
+  g = activation_functions.heaviside
+  epochs = 1e3
+
+  # create the estimator
+  estimator = estimators.Perceptron(n, g, epochs)
+
+  # train the estimator
+  w = estimator.train(x, d)
+
+  # evaluate the estimator
+  accuracy = estimator.evaluate(x, d, w)
+
+  # plot epoch vs error
+  plot_utils.line(estimator.plot_data_x, estimator.plot_data_y, x_label = 'epoch', y_label = 'error')
diff --git a/NeuralNetworks-python/setup.py b/NeuralNetworks-python/setup.py
new file mode 100644
index 0000000..026d25c
--- /dev/null
+++ b/NeuralNetworks-python/setup.py
@@ -0,0 +1,27 @@
+from distutils.util import convert_path
+from setuptools import setup, find_packages
+
+
+package_name = 'blueprint'
+package_info = {}
+
+with open(convert_path(f'{package_name}/package.py'), 'r') as file:
+  exec(file.read(), package_info)
+
+requirements = [
+  'numpy>=1.18.0',
+  'scipy>=1.5.4',
+  'matplotlib>=3.3.0'
+]
+
+setup(
+  name = package_name,
+  version = package_info['__version__'],
+  description = package_info['__description__'],
+  author = package_info['__author__'],
+  author_email = package_info['__email__'],
+  license = package_info['__license__'],
+  python_requires = '>=3.6.0',
+  install_requires = requirements,
+  packages = find_packages(),
+  zip_safe = False)