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Python
2016/6/7 Python
Python 3 3
Python
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• scikit-learn
• Numpy/Scipy
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scikit-learnmodel = SomeAlogrithm(hyperparameters)model.fit(x,y)prediction = model.predict(z)
model = SomeAlogrithm(hyperparameters)model.fit(x)prediction_x = model.labels_prediction_z = model.predict(z)
model = SomeAlogrithm(hyperparameters)model.fit(x)transformed = model.transform(z)
scikit-learn
n×m n×1 n
from sklearn import datasetsfrom sklearn.svm import SVC
iris=datasets.load_iris()data_train=iris.data[:-10,:]target_train=iris.target[:-10]data_eval=iris.data[-10:,:]target_eval=iris.target[-10:]
svc=SVC()svc.fit(data_train,target_train)predicted=svc.predict(data_eval)print("Accuracy: {}".format((target_eval==predicted).sum()/10.))
scikit-learn
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1 /1 Python i j (i,j)
0 1 23 4 56 7 89 10 11
a
1 [3,4,5]
0 [0,3,6,9]
(2,1) a[2,1] 1 a[1,:] 0 a[:,0]
(2,1) 7
>>> import numpy as np>>> a=np.arange(12).reshape(4,3)>>> aarray([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]])>>> a[1,:]array([3, 4, 5])>>> a[2,1]7>>> a[:,0]array([0, 3, 6, 9])>>>
csv 9 10
import numpy as npimport csv
data = []target = []filename = "input_data.csv"with open(filename) as f: for row in csv.reader(f): data.append([float(x) for x in row[:9]]) target.append(float(row[9]))
data = np.array(data)target = np.array(target)
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• np.array
MovieLens
from scipy import sparse
items = []users = []ratings = []for line in open("ml-100k/u.data"): a = line.split("\t") users.append(int(a[0])) items.append(int(a[1])) ratings.append(int(a[2]))
n_users = max(users)n_items = max(items)mat = sparse.lil_matrix((n_users, n_items))for u, i, r in zip(users, items, ratings): mat[u - 1, i - 1] = rmat = mat.tocsr()
• lil_matrix
• csr_matrix
scikit-learn
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scikit-learn
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• SVM SVC
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• SVM
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scikit-learn
np.meshgrid? np.c_? ravel?? ???
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model = SomeAlogrithm(hyperparameters)model.fit(x,y)prediction = model.predict(z)
• scikit-learn
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• scikit-learnnumpy matplotlib
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scikit-learn
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