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Travis CI: Add pytest --doctest-modules machine_learning (#1016)
* Travis CI: Add pytest --doctest-modules neural_network Fixes #987 ``` neural_network/perceptron.py:123: in <module> sample.insert(i, float(input('value: '))) ../lib/python3.7/site-packages/_pytest/capture.py:693: in read raise IOError("reading from stdin while output is captured") E OSError: reading from stdin while output is captured -------------------------------------------------------------------------------- Captured stdout -------------------------------------------------------------------------------- ('\nEpoch:\n', 399) ------------------------ value: ``` * Adding fix from #1056 -- thanks @QuantumNovice * if __name__ == '__main__': * pytest --ignore=virtualenv # do not test our dependencies
This commit is contained in:
Christian Clauss
GitHub
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commit
36684db278
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@ -1,124 +0,0 @@
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'''
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Perceptron
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w = w + N * (d(k) - y) * x(k)
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Using perceptron network for oil analysis,
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with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2
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p1 = -1
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p2 = 1
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'''
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from __future__ import print_function
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import random
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class Perceptron:
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def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1):
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self.sample = sample
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self.exit = exit
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self.learn_rate = learn_rate
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self.epoch_number = epoch_number
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self.bias = bias
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self.number_sample = len(sample)
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self.col_sample = len(sample[0])
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self.weight = []
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def trannig(self):
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for sample in self.sample:
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sample.insert(0, self.bias)
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for i in range(self.col_sample):
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self.weight.append(random.random())
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self.weight.insert(0, self.bias)
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epoch_count = 0
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while True:
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erro = False
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for i in range(self.number_sample):
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u = 0
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for j in range(self.col_sample + 1):
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u = u + self.weight[j] * self.sample[i][j]
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y = self.sign(u)
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if y != self.exit[i]:
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for j in range(self.col_sample + 1):
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self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j]
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erro = True
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#print('Epoch: \n',epoch_count)
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epoch_count = epoch_count + 1
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# if you want controle the epoch or just by erro
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if erro == False:
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print(('\nEpoch:\n',epoch_count))
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print('------------------------\n')
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#if epoch_count > self.epoch_number or not erro:
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break
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def sort(self, sample):
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sample.insert(0, self.bias)
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u = 0
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for i in range(self.col_sample + 1):
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u = u + self.weight[i] * sample[i]
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y = self.sign(u)
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if y == -1:
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print(('Sample: ', sample))
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print('classification: P1')
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else:
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print(('Sample: ', sample))
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print('classification: P2')
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def sign(self, u):
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return 1 if u >= 0 else -1
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samples = [
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[-0.6508, 0.1097, 4.0009],
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[-1.4492, 0.8896, 4.4005],
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[2.0850, 0.6876, 12.0710],
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[0.2626, 1.1476, 7.7985],
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[0.6418, 1.0234, 7.0427],
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[0.2569, 0.6730, 8.3265],
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[1.1155, 0.6043, 7.4446],
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[0.0914, 0.3399, 7.0677],
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[0.0121, 0.5256, 4.6316],
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[-0.0429, 0.4660, 5.4323],
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[0.4340, 0.6870, 8.2287],
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[0.2735, 1.0287, 7.1934],
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[0.4839, 0.4851, 7.4850],
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[0.4089, -0.1267, 5.5019],
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[1.4391, 0.1614, 8.5843],
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[-0.9115, -0.1973, 2.1962],
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[0.3654, 1.0475, 7.4858],
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[0.2144, 0.7515, 7.1699],
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[0.2013, 1.0014, 6.5489],
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[0.6483, 0.2183, 5.8991],
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[-0.1147, 0.2242, 7.2435],
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[-0.7970, 0.8795, 3.8762],
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[-1.0625, 0.6366, 2.4707],
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[0.5307, 0.1285, 5.6883],
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[-1.2200, 0.7777, 1.7252],
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[0.3957, 0.1076, 5.6623],
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[-0.1013, 0.5989, 7.1812],
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[2.4482, 0.9455, 11.2095],
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[2.0149, 0.6192, 10.9263],
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[0.2012, 0.2611, 5.4631]
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]
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exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
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network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
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network.trannig()
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while True:
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sample = []
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for i in range(3):
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sample.insert(i, float(input('value: ')))
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network.sort(sample)
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@ -1,17 +1,19 @@
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# Random Forest Classification
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# Importing the libraries
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importing the dataset
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dataset = pd.read_csv('Social_Network_Ads.csv')
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script_dir = os.path.dirname(os.path.realpath(__file__))
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dataset = pd.read_csv(os.path.join(script_dir, 'Social_Network_Ads.csv'))
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X = dataset.iloc[:, [2, 3]].values
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y = dataset.iloc[:, 4].values
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# Splitting the dataset into the Training set and Test set
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from sklearn.cross_validation import train_test_split
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
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# Feature Scaling
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@ -66,4 +68,4 @@ plt.title('Random Forest Classification (Test set)')
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plt.xlabel('Age')
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plt.ylabel('Estimated Salary')
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plt.legend()
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plt.show()
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plt.show()
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@ -1,12 +1,14 @@
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# Random Forest Regression
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# Importing the libraries
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importing the dataset
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dataset = pd.read_csv('Position_Salaries.csv')
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script_dir = os.path.dirname(os.path.realpath(__file__))
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dataset = pd.read_csv(os.path.join(script_dir, 'Position_Salaries.csv'))
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X = dataset.iloc[:, 1:2].values
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y = dataset.iloc[:, 2].values
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@ -28,7 +30,7 @@ regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)
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regressor.fit(X, y)
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# Predicting a new result
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y_pred = regressor.predict(6.5)
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y_pred = regressor.predict([[6.5]])
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# Visualising the Random Forest Regression results (higher resolution)
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X_grid = np.arange(min(X), max(X), 0.01)
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@ -38,4 +40,4 @@ plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
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plt.title('Truth or Bluff (Random Forest Regression)')
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plt.xlabel('Position level')
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plt.ylabel('Salary')
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plt.show()
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plt.show()
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@ -113,13 +113,13 @@ samples = [
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exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
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network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
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network.training()
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if __name__ == '__main__':
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network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
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network.training()
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while True:
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sample = []
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for i in range(3):
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sample.insert(i, float(input('value: ').strip()))
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sample.insert(i, float(input('value: ')))
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network.sort(sample)
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@ -7,6 +7,7 @@ opencv-python
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pandas
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pillow
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pytest
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requests
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sklearn
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sympy
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tensorflow
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