TakuTsuzuki

imagedir = getDirectory("Choose a Directory");
savedir = "/Users/tsuzuki/PycharmProjects/cell_detection/OkadaLab/dataset";

nfolder=12
j=0

for (k=1; k<=nfolder; k++){

    run("ROI Manager...");
    roiManager("Open", imagedir+"/"+k+"/neuron.zip");

TakuTsuzuki

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn import datasets

import brica1

TakuTsuzuki

# coding: utf-8


import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble, svm,datasets
 
import brica1

TakuTsuzuki

# coding: utf-8

import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble, datasets
 
import brica1


# RandomForeestClassifier Component Definition

TakuTsuzuki

import numpy as np

def entropy(X):
    #caluculate Entropy of 1D-list
    X = np.array(X,dtype='f')
    p = X/np.sum(X)
    p = np.array([x for x in p if x>0])
    
    H = np.sum(-p*np.log2(p))
    

TakuTsuzuki

"""
pysom.py is a python script for self-organizing map (SOM).
"""

import numpy as np
import matplotlib.pyplot as plt

# learning paras.
loop = 1000       # def: 1000
alpha_base = 1.0  # def: 1.0

TakuTsuzuki

import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def sigmoid_deriv(x):
    return x * (1 - x)

def softmax(x):
    temp = np.exp(x)

TakuTsuzuki

import numpy
import argparse
import cPickle as pickle

import utils

class Autoencoder3(object):
    def __init__(self, n_visible = 784, n_hidden1 = 784,
        n_hidden2 = 784, n_hidden3 = 784,
        n_hidden4 = 784, n_hidden5 = 784,