下面是python中的一個(gè)隨機(jī)森林實(shí)現(xiàn)。在

如果您需要它進(jìn)行圖像分割，我建議您使用ITKsnap，監(jiān)督學(xué)習(xí)，分割包，它使用隨機(jī)森林，并在python中實(shí)現(xiàn)。

這很簡單，你可以插入或定義你的標(biāo)簽和訓(xùn)練你的數(shù)據(jù)。你可以玩你的學(xué)習(xí)參數(shù)，如樹的數(shù)量或深度。

這是一個(gè)如何對大腦數(shù)據(jù)進(jìn)行分割的示例：import numpy as np

import csv as csv

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import GridSearchCV

from sklearn.cross_validation import StratifiedKFold # Add important libs

# Training:

train=[]

test=[] #Array Definition

path1 = r'D:\random forest\data set\train.csv' #Address Definition

path2 = r'D:\random forest\data set\test.csv'

with open(path1, 'r') as f1: #Open File as read by 'r'

reader = csv.reader(f1)

next(reader, None) #Skip header because file header is not needed

for row in reader: #fill array by file info by for loop

train.append(row)

train = np.array(train)

with open(path2, 'r') as f2:

reader2 = csv.reader(f2)

next(reader2, None)

for row2 in reader2:

test.append(row2)

test = np.array(test)

train = np.delete(train,[0],1)

test = np.delete(test,[0],1)

# Optimization

parameter_gridsearch = {

'max_depth' : [3, 4], #depth of each decision tree

'n_estimators': [50, 20], #count of decision tree

'max_features': ['sqrt', 'auto', 'log2'],

'min_samples_split': [2],

'min_samples_leaf': [1, 3, 4],

'bootstrap': [True, False],

}

# RF classification

randomForestClassifier()

crossvalidation = StratifiedKFold(train[0::,0] , n_folds=5)

gridsearch = GridSearchCV(randomforest, #grid search for algorithm optimization

scoring='accuracy',

param_grid=parameter_gridsearch,

cv=crossvalidation)

gridsearch.fit(train[0::,1::], train[0::,0]) #train[0::,0] is as target

model = gridsearch

parameters = gridsearch.best_params_

總結(jié)

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