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How to use test_pre method in avocado

Best Python code snippet using avocado_python

ocr.py

Source:ocr.py

1from PIL import Image2import pytesseract3import argparse4import cv25import os6import numpy as np7import re8import nltk9nltk.download('stopwords')10from nltk.corpus import stopwords11from nltk.stem.porter import PorterStemmer12from sklearn.feature_extraction.text import CountVectorizer13# load the example image and convert it to grayscale14image = cv2.imread("example6.png")15#cv2.imshow("image",image)16#cv2.waitKey(0)17#print type(image)18gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)19#cv2.imshow("grayscale",gray)20#cv2.waitKey(0)21#print type(gray)22_ ,gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)23#cv2.imshow("grayscale_tesh",gray)24#cv2.waitKey(0)25 26# make a check to see if median blurring should be done to remove27# noise28gray = cv2.medianBlur(gray, 3)29#cv2.imshow("grayscale_blurr",gray)30#cv2.waitKey(0)31 32# write the grayscale image to disk as a temporary file so we can33# apply OCR to it34filename = "{}.png".format(os.getpid())35print (filename)36cv2.imwrite(filename, gray)37# load the image as a PIL/Pillow image, apply OCR, and then delete38# the temporary file39text = pytesseract.image_to_string(Image.open(filename))40os.remove(filename)41print(text)42text=text.split()43test=' '.join(text)44x_test=[]45from nltk import tokenize46#nltk.download('punkt')47x_test=tokenize.sent_tokenize(test)48x_size=len(x_test)49###########################################################################################################50######## or or orr or    audio  speech #####################51import speech_recognition as sr52 53# obtain audio from the microphone54r = sr.Recognizer()55with sr.Microphone() as source:56    57     58    print("Say something!")59    audio = r.listen(source)60   61# recognize speech using Google Speech Recognition62    try:63         64        # for testing purposes, we're just using the default API key65        # to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`66        # instead of `r.recognize_google(audio)`67        x_test = r.recognize_google(audio)68        print("Google Speech Recognition thinks you said " + x_test)69    except sr.UnknownValueError:70         71        print("Google Speech Recognition could not understand audio")72    except sr.RequestError as e:73        print("Could not request results from Google Speech Recognition service; {0}".format(e))74    75    76###########################################################################################################77###########################################################################################################78######## emotion detection79from PIL import Image80import pytesseract81import argparse82import cv283import os84import numpy as np85import re86import nltk87nltk.download('stopwords')88from nltk.corpus import stopwords89from nltk.stem.porter import PorterStemmer90from sklearn.feature_extraction.text import CountVectorizer91###importing data sets92import pandas as pd93dataset=pd.read_csv('texttsv.tsv',delimiter='\t',encoding = "ISO-8859-1")94#dataset=pd.read_csv('Restaurant_Reviews.tsv',delimiter='\t',quoting=3)95##categorizing input96x=dataset.iloc[:,0]97y=dataset.iloc[:,1]98from sklearn.preprocessing import LabelEncoder,OneHotEncoder99label_y=LabelEncoder()100y_cat1=label_y.fit_transform(y)101onehotencoder=OneHotEncoder(categorical_features=[0])102y_cat=onehotencoder.fit_transform(y_cat1.reshape(-1,1)).toarray()103y_cat=y_cat[:,1:13]104##################################################################################105############ ########        train neural networks ############106ps=PorterStemmer()107corpus=[]108for i in range (0,40000):109    110    test_train=re.sub('[^a-zA-z]',' ',dataset['content'][i])111    test_train=test_train.lower()112    test_train=test_train.split()113    test_train=[ps.stem(word) for word in test_train   ]114    test_train=' '.join(test_train)115    corpus.append(test_train)116cv=CountVectorizer(max_features=30000)117x=cv.fit_transform(corpus).toarray()118#y_cat=y_cat[0:2000]119##################################################################################120####### spiltting121from sklearn.model_selection import train_test_split122X_train, X_test, Y_train, Y_test = train_test_split(x, y_cat, test_size = 0.2, random_state = 0)   123import keras124from keras.models import Sequential125#from.keras.models import Dense126from keras.layers.core import Dense127from keras.models import load_model128from keras.layers import Dropout129clasy=Sequential()130clasy.add(Dense(output_dim = 8000, init = 'uniform', activation = 'relu', input_dim=4000 ))131clasy.add(Dropout(rate = 0.3))132#clasy.add(Dense(output_dim = 600, init = 'uniform', activation = 'relu'))133#clasy.add(Dropout(rate = 0.1))134#clasy.add(Dense(output_dim = 3000, init = 'uniform', activation = 'relu'))135#clasy.add(Dropout(rate = 0.3))136clasy.add(Dense(output_dim = 8000, init = 'uniform', activation = 'relu'))137#clasy.add(Dense(output_dim = 4800, init = 'uniform', activation = 'relu'))138clasy.add(Dropout(rate = 0.3))139#clasy.add(Dense(output_dim = 12000, init = 'uniform', activation = 'relu'))140clasy.add(Dense(output_dim = 12, init = 'uniform', activation = 'sigmoid'))141clasy.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])142#clasy.fit(x,y_cat,batch_size=10,nb_epoch=15)143clasy.fit(X_train,Y_train,batch_size=10,nb_epoch=1)144#clasy=load_model("2000_3000_3000_12.h5")145#clasy.save('5k-4k-5k-12.h5')146y_pred=clasy.predict(X_test)147from sklearn.metrics import accuracy_score148accuracy_score(Y_test, y_pred.round())149#accuracy_score(np.array(Y_test, y_pred), np.ones((2, 2)))150########################### predict ing ###############################################151##nltk processing152ps=PorterStemmer()153combined_all=[0,0,0,0,0,0,0,0,0,0,0,0]154for m in range (0,x_size):155    corpus=[]156    for i in range (0,40000):157        test_train=re.sub('[^a-zA-z]',' ',dataset['content'][i])158        test_train=test_train.lower()159        test_train=test_train.split()160        test_train=[ps.stem(word) for word in test_train if not word in set(stopwords.words('english'))]161        test_train=' '.join(test_train)162        corpus.append(test_train)163    164    165#testing_prepossing166    test_pre=re.sub('[^a-zA-z]',' ',x_test[m])167    test_pre=test_pre.lower()168    test_pre=test_pre.split()169    test_pre=[ps.stem(word) for word in test_pre if not word in set(stopwords.words('english'))]170    test_pre=' '.join(test_pre)171    corpus.append(test_pre)   172 173#tokenizing174    175    cv=CountVectorizer(max_features=2000)176    x=cv.fit_transform(corpus).toarray()177#y=dataset.iloc[:,1].values178    x_pred_val=[]179    x_pred_val=x[40000:40001,0:2000]180    x=x[0:40000,0:2000]181#co rpus=x[0:40000,0:30000]182   183    184##predict185    y_pred=clasy.predict(x_pred_val)186   187   188    prob_pred=[]189    prob_pred1=[]190    for k in range (0,12):191        prob_pred=y_pred[0][k]192        prob_pred=prob_pred * 100193        prob_pred=float("{0:.2f}".format(prob_pred))194        prob_pred1.append(prob_pred)195    pred_val=[]196    pred_val=["boredom","empty","enthusiam","fun","happiness","hate","love","netural","relief","sadness","suprise","worry"]197#### to print it to i console #####198    #combined = np.vstack((pred_val, prob_pred1)).T199    #print('yout text was = ',x_test[m])200    #for a in range (0,12):201        #print(pred_val[a],'=',prob_pred1[a],'%')202        203    for q in range (0,12):204        combined_all[q]=combined_all[q] +  prob_pred1[q]205        206        207#################  printing to file #############208######## delete outputfilr if existed #########209    with open('outputfile.txt', 'a') as f:210        print(x_test[m],'\n',file=f)211        for r in range (0,12): 212            print(pred_val[r],'=',prob_pred1[r],'%', file=f)213        print('\n',file=f)214    f.close()215with open('outputfile.txt', 'a') as f:216    print('over all emotion','\n',file=f)   217    for w in range (0,12):  218        combined_all[w]=combined_all[w]/x_size219        combined_all[w]=float("{0:.2f}".format(  combined_all[w]))220        print(pred_val[w],'=',combined_all[w],'%', file=f)221f.close()...

data_splitter.py

Source:data_splitter.py

1import numpy as np2class Data_splitter(object):3    """docstring for Data_splitter"""4    def __init__(self):5        return678    def auto_split_data(self, num_valid, num_test, X, y):9        l = len(set(y))10        standard = self.cnt_labels_rate(y, l)11        # é¦ååå²åºéªè¯é12        valid_pre = self.lowest_square_idx(num_valid, y, l, standard)13        # print(valid_pre)14        # åå²15        X_valid = X[valid_pre:valid_pre+num_valid]16        y_valid = y[valid_pre:valid_pre+num_valid]17        # åå²åå©ä¸çyåå¹¶18        y_ = np.r_[y[:valid_pre], y[valid_pre+num_valid:]]19        # æµè¯é20        test_pre = self.lowest_square_idx(num_test, y_, l, standard)21        # print(test_pre)22        if (test_pre >= valid_pre):23            test_pre += num_valid24            test_bck = test_pre + num_test25        elif (test_pre + num_test > valid_pre):26            test_bck = num_valid + num_test + test_pre27        # print(test_pre)28        X_test = X[test_pre:test_bck]29        y_test = y[test_pre:test_bck]30        X_train, y_train = self.split_train(valid_pre, test_pre, num_valid, num_test, X, y)31        return X_train, X_valid, X_test, y_train, y_valid, y_test323334    # åå²è®ç»é35    def split_train(self, valid_pre, test_pre, num_valid, num_test, X, y):36        if test_pre >= valid_pre:37            pre_l = test_pre38            pre_s = valid_pre39            num_l = num_test40            num_s = num_valid41        elif (test_pre + num_test > valid_pre):42            X_train = np.r_[X[:test_pre], X[num_valid + num_test + test_pre:]]43            y_train = np.r_[y[:test_pre], y[num_valid + num_test + test_pre:]]44            return X_train, y_train45        else:46            pre_s = test_pre47            pre_l = valid_pre48            num_l = num_valid49            num_s = num_test50        X_train = np.r_[np.r_[X[:pre_s], X[pre_s+num_s:pre_l]], X[pre_l+num_l:]]51        y_train = np.r_[np.r_[y[:pre_s], y[pre_s+num_s:pre_l]], y[pre_l+num_l:]]52        return X_train, y_train535455    # åæ¹å¼æå°çä¸æ 56    def lowest_square_idx(self, num, y, l, standard):57        square = np.zeros(len(y) - num)58        for i in range(len(y) - num):59            # è®¡ç®ä¸æ åæ¹å·®æå°çä¸ç»æ°æ®60            w = (self.cnt_labels_rate(y[i:i + num], l) - standard)61            square[i] = w.dot(w.T)62        # æ¾å°æå°å¼çä¸æ 63        pre = np.argwhere(square == min(square))[0][0]64        return pre656667    # è®¡ç®å½åæ°æ®çåå¸æ¯ä¾68    def cnt_labels_rate(self, y, l):69        cnt = np.zeros(l)70        for i in range(l):71            cnt[i] = (y == i).sum() / y.shape[0]
...

stimuli_notebook.py

Source:stimuli_notebook.py

1from ExperimentSpecificCode._2017_03_28_Neuroseeker_Auditory_Double.Stimulus import arnes_basic_analysis as ba2data_path = r'F:\Neuroseeker\Neuroseeker_2017_03_28_Anesthesia_Auditory_DoubleProbes'3overwrite = True4binwidth = 0.055stimulus = 'ToneSequence'6pre = 37post = 38plot_type = 'psth'9test_pre = 0.410test_post = 0.111min_rate = 0.512ba.tuning(data_path=data_path, overwrite=overwrite, binwidth=binwidth, stimulus=stimulus, frequencies_index=[0, 1, 2, 3],13          pre=pre, post=post, plot_type=plot_type, test_pre=test_pre,  test_post=test_post, min_rate=min_rate)14overwrite = True15binwidth = 0.0516pre = 217post = 218test_pre = 0.519test_post = 0.220min_rate = 221ba.tones(data_path=data_path, overwrite=overwrite, binwidth=binwidth, pre=pre, post=post,22          test_pre=test_pre, test_post=test_post, min_rate=min_rate)23template = 12024df = pd.DataFrame(index=[template])25try:26    temp = df['a'].loc[template]27    temp[7, :] = np.ones(100) *528except:29    temp = np.empty((10, 100))30    temp[0, :] = np.ones(100) * 331finally:...

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