随笔分类 - Python3机器学习
摘要：import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltfrom sklearn import datasetsraw_data_X = [[3.393533211, 2.331273381], [3.1100
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摘要：import numpy as npimport matplotlib.pyplot as pltdef sigmoid(t): return 1 / (1 + np.exp(-t))x = np.linspace(-10,10,500)print(x)y = sigmoid(x)plt.plot(
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摘要：import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasetsiris = datasets.load_iris()X = iris.datay = iris.targetX = X[y<2,:2]#prin
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摘要：import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltfrom sklearn import datasetsclass train_test_split1: def __init__(self): prin
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摘要：import numpy as npimport matplotlib.pyplot as pltplot_x = np.linspace(-1,6,141)plot_y = (plot_x - 2.5)**2 -1#plt.plot(plot_x,plot_y)#plt.show()#损失函数求导
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摘要：import numpy as npimport matplotlib.pyplot as pltnp.random.seed(666)x = 2 * np.random.random(size=100)y = x*3. + 4. + np.random.normal(size=100)X = x.
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摘要：import numpy as npimport matplotlib.pyplot as pltx = np.random.uniform(-3,3, size=100)X = x.reshape(-1,1)y =0.5 * x**2 + x + np.random.normal(0,1,size
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摘要：import numpy as npimport matplotlib.pyplot as pltX = np.empty((100,2))X[:,0] = np.random.uniform(0,100,size=100)X[:,1] = 0.75 * X[:,0] + 3. + np.rando
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摘要：import numpy as npfrom dev.metrics import accuracy_scoreclass LogisticRegression: def __init__(self): """初始化Linear Regression模型""" self.coef_ = None s
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摘要：import numpy as npfrom .metrics import r2_scoreclass SimpleLinearRegression: def __init__(self): """初始化Simple Linear Regression模型""" self.a_ = None se
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摘要：import numpy as npclass StandardScaler: def __init__(self): self.mean_ = None self.scale_ = None def fit(self, X): """根据训练数据集X获得数据的均值和方差""" assert X.n
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摘要：import numpy as npdef train_test_split(X, y, test_ratio=0.2, seed=None): """将数据 X 和 y 按照test_ratio分割成X_train, X_test, y_train, y_test""" assert X.shap
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摘要：import numpy as npclass PCA: def __init__(self, n_components): """初始化PCA""" assert n_components >= 1, "n_components must be valid" self.n_components =
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摘要：import numpy as npfrom math import sqrtdef accuracy_score(y_true, y_predict): """计算y_true和y_predict之间的准确率""" assert len(y_true) == len(y_predict), \ "
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摘要：import numpy as npfrom .metrics import r2_scoreclass LinearRegression: def __init__(self): """初始化Linear Regression模型""" self.coef_ = None self.interce
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摘要：import numpy as npfrom math import sqrtfrom collections import Counterfrom .metrics import accuracy_scoreclass KNNClassifier: def __init__(self, k): "
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