#K-NN分类

import os

import sys

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable \

    from menzhenZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        lables.append(row[5])

    return np.array(dataset),np.array(lables)

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from \

    testZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        testlables.append(row[5])

    return np.array(testdataset),np.array(testlables)

#K-NN分类

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

#归一化

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))

    return normDataSet, ranges, minVals

erace = []

accuc = []

t = []

#启动和检测模型

def datingClassTest():

    datingDataMat,datingLabels = getdata("外科","胸外科")

    normMat, ranges, minVals = autoNorm(datingDataMat)

    testdataset,testlables = gettestdata("外科","胸外科")

    testnormMat, testranges, testminVals = autoNorm(testdataset)

    errorCount = 0.0

    start = time.time()

    for j in [3,5,7,9,11,13]:

        for i in range(np.shape(testnormMat)[0]):

            classifierResult = classify0(testnormMat[i,:],normMat,datingLabels,j)

            print("the classifier came back with: %s, the real answer is: %s" % (classifierResult, testlables[i]))

            if (classifierResult != testlables[i]):

                errorCount += 1.0

        end = time.time()

        t.append(end)

        erace.append(errorCount/float(np.shape(testnormMat)[0])*100)

        accuc.append((1.0-errorCount/float(np.shape(testnormMat)[0]))*100)

        print("错误率: %.2f%%" % (errorCount/float(np.shape(testnormMat)[0])*100))

        print("准确率: %.2f%%" % ((1.0-errorCount/float(np.shape(testnormMat)[0]))*100))

        print("训练和预测一共耗时: %.2f 秒" % (end-start))

datingClassTest()

print(accuc)

print(erace)

print(t)

#探索不同的K值对算法的影响

import matplotlib.pyplot as plt

x = [3,5,7,9,11,13]

plt.plot(x,erace,c='r')

plt.plot(x,accuc,c='g')

plt.legend(['error race','accuce race'],loc=9)

plt.show()

print(accuc)

print(erace)

#决策树

import os

import sys

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

from math import log

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhenZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        temp.append(row[5])

        dataset.append(temp)

    lables = []

    lables.append("呼吸急促")

    lables.append("持续性脉搏加快")

    lables.append("畏寒")

    lables.append("血压降低")

    lables.append("咳血")

    return dataset,lables

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from testZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        testlables.append(row[5])

    return testdataset,testlables

#计算熵值

def calcShannonEnt(dataSet):

    numEntries = len(dataSet)

    labelCounts = {}

    for featVec in dataSet:

        currentLabel = featVec[-1]

        if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0

        labelCounts[currentLabel] += 1

    shannonEnt = 0.0

    for key in labelCounts:

        prob = float(labelCounts[key])/numEntries

        shannonEnt -= prob * log(prob,2)

    return shannonEnt

#按照给定特征划分数据集

def splitDataSet(dataSet, axis, value):

    retDataSet = []

    for featVec in dataSet:

        if featVec[axis] == value:

            reducedFeatVec = featVec[:axis]

            reducedFeatVec.extend(featVec[axis+1:])

            retDataSet.append(reducedFeatVec)

    return retDataSet

#选择最好的属性

def chooseBestFeatureToSplit(dataSet):

    numFeatures = len(dataSet[0]) - 1

    baseEntropy = calcShannonEnt(dataSet)

    bestInfoGain = 0.0

    bestFeature = -1

    for i in range(numFeatures):

        featList = [example[i] for example in dataSet]

        uniqueVals = set(featList)

        newEntropy = 0.0

        for value in uniqueVals:

            subDataSet = splitDataSet(dataSet, i, value)

            prob = len(subDataSet)/float(len(dataSet))

            newEntropy += prob * calcShannonEnt(subDataSet)

        infoGain = baseEntropy - newEntropy

        if (infoGain > bestInfoGain):

            bestInfoGain = infoGain

            bestFeature = i

    return bestFeature                     

#统计机制

def majorityCnt(classList):

    classCount={}

    for vote in classList:

        if vote not in classCount.keys(): classCount[vote] = 0

        classCount[vote] += 1

    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

#创建决策树

def createTree(dataSet,labels):

    classList = [example[-1] for example in dataSet]

    if classList.count(classList[0]) == len(classList):

        return classList[0]

    if len(dataSet[0]) == 1:

        return majorityCnt(classList)

    bestFeat = chooseBestFeatureToSplit(dataSet)

    bestFeatLabel = labels[bestFeat]

    myTree = {bestFeatLabel:{}}

    temp = []

    for i in labels:

        if i != labels[bestFeat]:

            temp.append(i)

    labels = temp

    featValues = [example[bestFeat] for example in dataSet]

    uniqueVals = set(featValues)

    for value in uniqueVals:

        subLabels = labels[:]

        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)

    return myTree   

#使用决策树模型分类

def classify(inputTree,featLabels,testVec):

    for i in inputTree.keys():

        firstStr = i

        break

    secondDict = inputTree[firstStr]

    featIndex = featLabels.index(firstStr)

    key = testVec[featIndex]

    valueOfFeat = secondDict[key]

    if isinstance(valueOfFeat, dict):

        classLabel = classify(valueOfFeat, featLabels, testVec)

    else: classLabel = valueOfFeat

    return classLabel

#启动和检测模型

def datingClassTest():

    dataSet,labels = getdata("外科","胸外科")

    myTree = createTree(dataSet,labels)

    testdataset,testlables = gettestdata("外科","胸外科")

    errorCount = 0.0

    start = time.time()

    for i in range(np.shape(testdataset)[0]):

        classifierResult = classify(myTree,labels,testdataset[i])

        print("the classifier came back with: %s, the real answer is: %s" % (classifierResult, testlables[i]))

        if (classifierResult != testlables[i]):

            errorCount += 1.0

    end = time.time()

    print("错误率: %.2f%%" % (errorCount/float(np.shape(testdataset)[0])*100))

    print("准确率: %.2f%%" % ((1.0-errorCount/float(np.shape(testdataset)[0]))*100))

    print("训练和预测一共耗时: %.2f 秒" % (end-start))

datingClassTest()

#选取前600条记录生成并打印决策树

dataSet,labels = getdata("外科","胸外科")

dataSet = dataSet[0:600]

labels = labels[0:600]

myTree = createTree(dataSet,labels)

print(myTree)

#比较K-NN算法与决策树算法的优劣

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

x = np.array([10,12])

y = [85.6,87.3]

plt.bar(x,y,edgecolor='yellow')

for i,j in zip(x,y):

    plt.text(i-0.2,j-0.2,'%.2f%%' % j)

plt.text(9.7,40,'K-NN right race')

plt.text(11.7,40,'Tree right race')

plt.show()

#使用神经网络探索数据集

import sys

import os

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#one-hot编码

def onehot(labels):

    n_sample = len(labels)

    n_class = max(labels) + 1

    onehot_labels = np.zeros((n_sample, n_class))

    onehot_labels[np.arange(n_sample), labels] = 1

    return onehot_labels

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        if(row[5]==3):

            lables.append(0)

        elif(row[5]==6):

            lables.append(1)

        else:

            lables.append(2)

    dataset = np.array(dataset)

    lables = np.array(lables)

    dataset = dataset.astype(np.float32)

    labless = onehot(lables)

    return dataset,labless

#获取测试数据集

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from test where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        if(row[5]==3):

            testlables.append(0)

        elif(row[5]==6):

            testlables.append(1)

        else:

            testlables.append(2)

    testdataset = np.array(testdataset)

    testlables = np.array(testlables)

    testdataset = testdataset.astype(np.float32)

    testlabless = onehot(testlables)

    return testdataset,testlabless

dataset,labless = getdata("外科","胸外科")

testdataset,testlables = gettestdata("外科","胸外科")

dataset = dataset[0:100]

labless = labless[0:100]

x_data = tf.placeholder("float32", [None, 5])

y_data = tf.placeholder("float32", [None, 3])

weight = tf.Variable(tf.ones([5, 3]))

bias = tf.Variable(tf.ones([3]))

#使用softmax激活函数

y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)

#y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)

# loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

#使用交叉熵作为损失函数

loss = -tf.reduce_sum(y_data*tf.log(y_model))

# train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

#使用AdamOptimizer优化器

train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

#train_step = tf.train.MomentumOptimizer(1e-4,0.9).minimize(loss)

#评估模型

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

start = time.time()

for _ in range(10):

    for i in range(int(len(dataset)/100)):

        sess.run(train_step, feed_dict={x_data:dataset[i:i+100,:], y_data:labless[i:i+100,:]})

print("模型准确率",sess.run(accuracy, feed_dict={x_data:testdataset , y_data:testlables}))

end = time.time()

print("模型训练和测试公耗时：%.2f 秒" % (end-start))

#加深一层神经网络

import sys

import os

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#one-hot编码

def onehot(labels):

    n_sample = len(labels)

    n_class = max(labels) + 1

    onehot_labels = np.zeros((n_sample, n_class))

    onehot_labels[np.arange(n_sample), labels] = 1

    return onehot_labels

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        if(row[5]==3):

            lables.append(0)

        elif(row[5]==6):

            lables.append(1)

        else:

            lables.append(2)

    dataset = np.array(dataset)

    lables = np.array(lables)

    dataset = dataset.astype(np.float32)

    labless = onehot(lables)

    return dataset,labless

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from test where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        if(row[5]==3):

            testlables.append(0)

        elif(row[5]==6):

            testlables.append(1)

        else:

            testlables.append(2)

    testdataset = np.array(testdataset)

    testlables = np.array(testlables)

    testdataset = testdataset.astype(np.float32)

    testlabless = onehot(testlables)

    return testdataset,testlabless

dataset,labless = getdata("外科","胸外科")

testdataset,testlables = gettestdata("外科","胸外科")

dataset = dataset[0:100]

labless = labless[0:100]

x_data = tf.placeholder("float32", [None, 5])

y_data = tf.placeholder("float32", [None, 3])

weight1 = tf.Variable(tf.ones([5, 20]))

bias1 = tf.Variable(tf.ones([20]))

y_model1 = tf.matmul(x_data, weight1) + bias1

#加深一层神经网络

weight2 = tf.Variable(tf.ones([20, 3]))

bias2 = tf.Variable(tf.ones([3]))

y_model = tf.nn.softmax(tf.matmul(y_model1, weight2) + bias2)

loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

# loss = -tf.reduce_sum(y_data*tf.log(y_model))

#train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

# train_step = tf.train.MomentumOptimizer(1e-4,0.9).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

start = time.time()

for _ in range(10):

    for i in range(int(len(dataset)/100)):

        sess.run(train_step, feed_dict={x_data:dataset[i:i+100,:], y_data:labless[i:i+100,:]})

print("模型准确率",sess.run(accuracy, feed_dict={x_data:testdataset , y_data:testlables}))

end = time.time()

print("模型训练和测试公耗时：%.2f 秒" % (end-start))

#比较决策树与神经网络的优劣

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

x = np.array([10,12])

y = [87.1,87.4]

plt.bar(x,y,edgecolor="yellow")

for i,j in zip(x,y):

    plt.text(i-0.2,j-0.2,"%.2f%%" % j)

plt.text(9.7,40,"Tree right race")

plt.text(11.7,40,"Net right race")

plt.scatter([9.7,11.7],[0.05,0.36],c="r")

plt.plot([9.7,11.7],[0.05,0.36],c="g")

plt.show()

#统计各种算法处理模型数据

K-NN算法：

当K取[3,5,7,9,11,13]时，对应的：

准确率：[85.6, 72.6, 60.0, 47.4, 34.8, 22.299999999999996]

总耗时：[1554119134.435363, 1554119136.6192698,

     1554119138.846019, 1554119141.2507513, 1554119143.4782736, 1554119145.5415804]

决策树：

准确率: 87.10%

训练和预测一共耗时: 0.05 秒

神经网络设计：

1 最小二乘法 softmax GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.16 秒

2 最小二乘法 softmax AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.19 秒

3 最小二乘法 softmax MomentumOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.18 秒

4 最小二乘法 relu GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.17 秒

5 最小二乘法 relu AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.15 秒

6 最小二乘法 relu MomentumOptimizer 模型

模型准确率 0.006

模型训练和测试公耗时：0.19 秒

7 交叉熵 softmax GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.09 秒

8 交叉熵 softmax AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.08 秒

9 交叉熵 softmax MomentumOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.06 秒

10 交叉熵 relu GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.08 秒

11 交叉熵 relu AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.08 秒

12 交叉熵 relu MomentumOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.09 秒

从上面的12种神经网络设计模型中可以看出：最小二乘法 relu MomentumOptimizer 模型

的准确率只有0.006，所以这种模型的设计是失败的。

a = [0.874]*10

print(a)
#计算成功的各种神经网络模型的准确率与耗时的比值：

a = [0.874]*11

b = [0.16,0.19,0.18,0.17,0.15,0.09,0.08,0.06,0.08,0.09,0.09]

c = []

for i in range(len(a)):

    c.append(a[i]/b[i])

for i in range(len(c)):

    print("准确率与耗时的比值:%.4f" % (c[i]))

#K-NN算法

#当K取3、5、7、9、11、13时的准确率饼图分布显示

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

acc = [85.6, 72.6, 60.0, 47.4, 34.8, 22.2]

labels = ['K-3','K-5','K-7','K-9','K-11','K-13']

plt.pie(acc,labels=labels,shadow=True,startangle=90,autopct='%1.4f%%')

plt.axis('equal')

plt.title('K-NN',fontsize=25)

plt.show()

#K-NN算法耗时散点图

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

x = np.array([1,2,3,4,5,6])

z = np.array([1554119134.435363, 1554119136.6192698,1554119138.846019,

              1554119141.2507513, 1554119143.4782736, 1554119145.5415804])

plt.scatter(x,z,c='g')

plt.xticks(x+0.4,['KNN-1','KNN-2','KNN-3','KNN-4','KNN-5','KNN-6'])

plt.show()

#神经网络算法对应各种有用的模型设计耗时曲线图

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

x = np.array([1,2,3,4,5,6,7,8,9,10,11])

z = np.array([0.16,0.19,0.18,0.17,0.15,0.09,0.08,0.06,0.08,0.09,0.09])

plt.scatter(x,z,c='r')

plt.xticks(x+0.4,['NET-1','NET-2','NET-3','NET-4','NET-5',

                 'NET-6','NET-7','NET-8','NET-9','NET-10','NET-11'])

plt.show()

#K-NN、决策树以及神经网络算法对比

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

acc = [85.6, 72.6, 60.0, 47.4, 34.8, 22.2,87.10,0.874,

       87.4,87.4,87.4,87.4,87.4,87.4,87.4,87.4,87.4,87.4]

labels = ['K-3','K-5','K-7','K-9','K-11','K-13','TREE',

          'NET-1','NET-2','NET-3','NET-4','NET-5','NET-6','NET-7',

         'NET-8','NET-9','NET-10','NET-11']

explode = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0]

plt.pie(acc,labels=labels,explode=explode,shadow=True,startangle=90,autopct='%1.4f%%')

plt.axis('equal')

plt.title('K-NN AND TREE AND NET',fontsize=25)

plt.show()

吴裕雄 python 人工智能——基于神经网络算法在智能医疗诊断中的应用探索代码简要展示的更多相关文章

  1. 吴裕雄 PYTHON 人工智能——基于MASK_RCNN目标检测（5）

    import os import sys import numpy as np import tensorflow as tf import matplotlib import matplotlib. ...

  2. 吴裕雄 PYTHON 人工智能——基于MASK_RCNN目标检测（4）

    import os import sys import random import math import re import time import numpy as np import tenso ...

  3. 吴裕雄 python 人工智能——基于Mask_RCNN目标检测（3）

    import os import sys import random import math import re import time import numpy as np import cv2 i ...

  4. 吴裕雄 python 人工智能——基于Mask_RCNN目标检测（2）

    import os import sys import itertools import math import logging import json import re import random ...

  5. 吴裕雄 python 人工智能——基于Mask_RCNN目标检测（1）

    import os import sys import random import math import numpy as np import skimage.io import matplotli ...

  6. 吴裕雄 python 人工智能——智能医疗系统后台用户注册、登录和初诊简约版代码展示

    #用户注册.登录模块 #数据库脚本 CREATE TABLE usertable( userid number(8) primary key not null , username varchar(5 ...

  7. 吴裕雄 python 机器学习——人工神经网络感知机学习算法的应用

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

  8. 吴裕雄 python 机器学习——人工神经网络与原始感知机模型

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

  9. 吴裕雄 PYTHON 人工智能——智能医疗系统后台智能分诊模块及系统健康养生公告简约版代码展示

    #coding:utf-8 import sys import cx_Oracle import numpy as np import pandas as pd import tensorflow a ...

随机推荐

  1. 生成器和迭代器_python

    一.生成器简介(generator) 在进行较大数据的存储,如果直接存储在列表之中,则会可能造成内存的不够与速度的减慢,因为列表创建完是立即创建并存在的,而在python中生成器(generator) ...

  2. html中多选框变单选框

    就是一个小js,直接上代码 $("input[name='checkboxlist']").each(function () { if (this.checked &&am ...

  3. mongo备份操作

    数据备份mongodump 可以用mongodump 来做MongoDB 的库或表级别的备份,下面举例说明: >c:\mongo\bin\mongodump -d  xxxx数据库 此时会在当前 ...

  4. 1060 Are They Equal (25分)

    1060 Are They Equal (25分) 题目 思路 定义结构体 struct fraction{ string f; int index; } 把输入的两个数先都转换为科学计数法,统一标准 ...

  5. 使用命令把SpringBoot项目打包成可运行的jar包（简洁，操作性强）

    前几天接到一个需求,要把原系统中一个数据处理小功能搬出原系统,拉出来单独做一个SpringBoot项目,然后打成jar包扔到Windows服务器上运行,这样数据处理的时候如果遇到堵塞就不至于整个系统都 ...

  6. Docker - 最近的踩到的一些坑

    概述 最近学习 docker 遇到的 坑 1. dockerfile: 安装命令 概述 安装命令 坑 选项参数里, 一定要 带 -y 不带的话, 基本会阻塞构建 2. 其他: 处理问题, 一定不能慌 ...

  7. Android Studio 初级控件笔记

    Android支持的像素单位 Android支持的像素单位有:px(像素).in(英寸).mm(毫米).pt(磅,1/72英寸).dp(与设备无关的显示单位).dip(就是dp).sp(用于设置字体大 ...

  8. Linux上查看当前系统各内存分区信息

    命令 ulimit -a -a 查看所有信息,同理,也可以例如 ulimit -s 只查看栈占内存信息

  9. css的理解 ----footrt固定在底部

    <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8&quo ...

  10. Flask 教程 第十六章：全文搜索

    本文翻译自The Flask Mega-Tutorial Part XVI: Full-Text Search 这是Flask Mega-Tutorial系列的第十六部分,我将在其中为Microblo ...