Saturday, April 4, 2020

How to Install TensorFlow with GPU Support on Windows 10 Notebook



Need Windows 10, 64 bits (Home Edition is OK) and NVIDIA Graphics Card with minimum Cuda capability of 3.0(2GB Graphic RAM, or more is better for larger dataset), i5/i7 Notebook with 8GB RAM (more is better) are fine. The latest gaming i7 laptop typically has 16GB RAM or more and NVIDIA Card with 6GB graphics.

This installation guide works for my i7 SAMSUNG notebook with GT 650M.

How to Install TensorFlow with GPU Support on Windows 10


After the installation, and go into tf-gpu python environment
conda activate tf-gpu
test run the followings
python tftest.py    Select all
#!/usr/bin/env python import tensorflow as tf with tf.compat.v1.Session() as sess: hello = tf.constant('Hello, TensorFlow! '+ tf.__version__) print (sess.run(hello).decode()) sess.close()



The following irislearn.py requires the additional installation of python modules
pip install matplotlib pandas sklearn
and download the iris.data from
https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
or create the iris.data file from the content below
python irislearn.py    Select all
#!/usr/bin/env python # Check the versions of libraries # Python version import sys print('Python: {}'.format(sys.version)) # scipy import scipy print('scipy: {}'.format(scipy.__version__)) # numpy import numpy print('numpy: {}'.format(numpy.__version__)) # matplotlib import matplotlib print('matplotlib: {}'.format(matplotlib.__version__)) # pandas import pandas print('pandas: {}'.format(pandas.__version__)) # scikit-learn import sklearn print('sklearn: {}'.format(sklearn.__version__)) # Load libraries import pandas from pandas.plotting import scatter_matrix import matplotlib.pyplot as plt from sklearn import model_selection from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC # Load dataset #url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" url = "iris.data" names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class'] dataset = pandas.read_csv(url, names=names) # shape print(dataset.shape) # head print(dataset.head(20)) # descriptions print(dataset.describe()) # class distribution print(dataset.groupby('class').size()) # box and whisker plots dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False) plt.suptitle("Box and Whisker Plots for inputs") plt.show() # histograms dataset.hist() plt.suptitle('Histograms for inputs') plt.show() # scatter plot matrix scatter_matrix(dataset) plt.suptitle('Scatter Plot Matrix for inputs') plt.show() # Split-out validation dataset array = dataset.values X = array[:,0:4] Y = array[:,4] validation_size = 0.20 seed = 7 X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed) # Test options and evaluation metric seed = 7 scoring = 'accuracy' # Spot Check Algorithms models = [] models.append(('LR', LogisticRegression(max_iter=10000))) models.append(('LDA', LinearDiscriminantAnalysis())) models.append(('KNN', KNeighborsClassifier())) models.append(('CART', DecisionTreeClassifier())) models.append(('NB', GaussianNB())) models.append(('SVM', SVC())) # evaluate each model in turn results = [] names = [] for name, model in models: kfold = model_selection.KFold(n_splits=10, random_state=seed, shuffle=True) cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) results.append(cv_results) names.append(name) msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) print(msg) # Compare Algorithms fig = plt.figure() fig.suptitle('Algorithm Comparison') ax = fig.add_subplot(111) plt.boxplot(results) ax.set_xticklabels(names) plt.show() # Make predictions on validation dataset knn = KNeighborsClassifier() knn.fit(X_train, Y_train) predictions = knn.predict(X_validation) print(accuracy_score(Y_validation, predictions)) print(confusion_matrix(Y_validation, predictions)) print(classification_report(Y_validation, predictions)) print("Make predictions on LogisticRegression Model") model = LogisticRegression(max_iter=10000) model.fit(X_train, Y_train) predictions = model.predict(X_validation) print(accuracy_score(Y_validation, predictions)) print(confusion_matrix(Y_validation, predictions)) print(classification_report(Y_validation, predictions)) # print prediction results on test data for i, prediction in enumerate(predictions): print ('Predicted: %s, Target: %s %s' % (prediction, Y_validation[i], '' if prediction==Y_validation[i] else '(WRONG!!!)'))



iris.data
iris.data    Select all 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa 5.4,3.9,1.7,0.4,Iris-setosa 4.6,3.4,1.4,0.3,Iris-setosa 5.0,3.4,1.5,0.2,Iris-setosa 4.4,2.9,1.4,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 5.4,3.7,1.5,0.2,Iris-setosa 4.8,3.4,1.6,0.2,Iris-setosa 4.8,3.0,1.4,0.1,Iris-setosa 4.3,3.0,1.1,0.1,Iris-setosa 5.8,4.0,1.2,0.2,Iris-setosa 5.7,4.4,1.5,0.4,Iris-setosa 5.4,3.9,1.3,0.4,Iris-setosa 5.1,3.5,1.4,0.3,Iris-setosa 5.7,3.8,1.7,0.3,Iris-setosa 5.1,3.8,1.5,0.3,Iris-setosa 5.4,3.4,1.7,0.2,Iris-setosa 5.1,3.7,1.5,0.4,Iris-setosa 4.6,3.6,1.0,0.2,Iris-setosa 5.1,3.3,1.7,0.5,Iris-setosa 4.8,3.4,1.9,0.2,Iris-setosa 5.0,3.0,1.6,0.2,Iris-setosa 5.0,3.4,1.6,0.4,Iris-setosa 5.2,3.5,1.5,0.2,Iris-setosa 5.2,3.4,1.4,0.2,Iris-setosa 4.7,3.2,1.6,0.2,Iris-setosa 4.8,3.1,1.6,0.2,Iris-setosa 5.4,3.4,1.5,0.4,Iris-setosa 5.2,4.1,1.5,0.1,Iris-setosa 5.5,4.2,1.4,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 5.0,3.2,1.2,0.2,Iris-setosa 5.5,3.5,1.3,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 4.4,3.0,1.3,0.2,Iris-setosa 5.1,3.4,1.5,0.2,Iris-setosa 5.0,3.5,1.3,0.3,Iris-setosa 4.5,2.3,1.3,0.3,Iris-setosa 4.4,3.2,1.3,0.2,Iris-setosa 5.0,3.5,1.6,0.6,Iris-setosa 5.1,3.8,1.9,0.4,Iris-setosa 4.8,3.0,1.4,0.3,Iris-setosa 5.1,3.8,1.6,0.2,Iris-setosa 4.6,3.2,1.4,0.2,Iris-setosa 5.3,3.7,1.5,0.2,Iris-setosa 5.0,3.3,1.4,0.2,Iris-setosa 7.0,3.2,4.7,1.4,Iris-versicolor 6.4,3.2,4.5,1.5,Iris-versicolor 6.9,3.1,4.9,1.5,Iris-versicolor 5.5,2.3,4.0,1.3,Iris-versicolor 6.5,2.8,4.6,1.5,Iris-versicolor 5.7,2.8,4.5,1.3,Iris-versicolor 6.3,3.3,4.7,1.6,Iris-versicolor 4.9,2.4,3.3,1.0,Iris-versicolor 6.6,2.9,4.6,1.3,Iris-versicolor 5.2,2.7,3.9,1.4,Iris-versicolor 5.0,2.0,3.5,1.0,Iris-versicolor 5.9,3.0,4.2,1.5,Iris-versicolor 6.0,2.2,4.0,1.0,Iris-versicolor 6.1,2.9,4.7,1.4,Iris-versicolor 5.6,2.9,3.6,1.3,Iris-versicolor 6.7,3.1,4.4,1.4,Iris-versicolor 5.6,3.0,4.5,1.5,Iris-versicolor 5.8,2.7,4.1,1.0,Iris-versicolor 6.2,2.2,4.5,1.5,Iris-versicolor 5.6,2.5,3.9,1.1,Iris-versicolor 5.9,3.2,4.8,1.8,Iris-versicolor 6.1,2.8,4.0,1.3,Iris-versicolor 6.3,2.5,4.9,1.5,Iris-versicolor 6.1,2.8,4.7,1.2,Iris-versicolor 6.4,2.9,4.3,1.3,Iris-versicolor 6.6,3.0,4.4,1.4,Iris-versicolor 6.8,2.8,4.8,1.4,Iris-versicolor 6.7,3.0,5.0,1.7,Iris-versicolor 6.0,2.9,4.5,1.5,Iris-versicolor 5.7,2.6,3.5,1.0,Iris-versicolor 5.5,2.4,3.8,1.1,Iris-versicolor 5.5,2.4,3.7,1.0,Iris-versicolor 5.8,2.7,3.9,1.2,Iris-versicolor 6.0,2.7,5.1,1.6,Iris-versicolor 5.4,3.0,4.5,1.5,Iris-versicolor 6.0,3.4,4.5,1.6,Iris-versicolor 6.7,3.1,4.7,1.5,Iris-versicolor 6.3,2.3,4.4,1.3,Iris-versicolor 5.6,3.0,4.1,1.3,Iris-versicolor 5.5,2.5,4.0,1.3,Iris-versicolor 5.5,2.6,4.4,1.2,Iris-versicolor 6.1,3.0,4.6,1.4,Iris-versicolor 5.8,2.6,4.0,1.2,Iris-versicolor 5.0,2.3,3.3,1.0,Iris-versicolor 5.6,2.7,4.2,1.3,Iris-versicolor 5.7,3.0,4.2,1.2,Iris-versicolor 5.7,2.9,4.2,1.3,Iris-versicolor 6.2,2.9,4.3,1.3,Iris-versicolor 5.1,2.5,3.0,1.1,Iris-versicolor 5.7,2.8,4.1,1.3,Iris-versicolor 6.3,3.3,6.0,2.5,Iris-virginica 5.8,2.7,5.1,1.9,Iris-virginica 7.1,3.0,5.9,2.1,Iris-virginica 6.3,2.9,5.6,1.8,Iris-virginica 6.5,3.0,5.8,2.2,Iris-virginica 7.6,3.0,6.6,2.1,Iris-virginica 4.9,2.5,4.5,1.7,Iris-virginica 7.3,2.9,6.3,1.8,Iris-virginica 6.7,2.5,5.8,1.8,Iris-virginica 7.2,3.6,6.1,2.5,Iris-virginica 6.5,3.2,5.1,2.0,Iris-virginica 6.4,2.7,5.3,1.9,Iris-virginica 6.8,3.0,5.5,2.1,Iris-virginica 5.7,2.5,5.0,2.0,Iris-virginica 5.8,2.8,5.1,2.4,Iris-virginica 6.4,3.2,5.3,2.3,Iris-virginica 6.5,3.0,5.5,1.8,Iris-virginica 7.7,3.8,6.7,2.2,Iris-virginica 7.7,2.6,6.9,2.3,Iris-virginica 6.0,2.2,5.0,1.5,Iris-virginica 6.9,3.2,5.7,2.3,Iris-virginica 5.6,2.8,4.9,2.0,Iris-virginica 7.7,2.8,6.7,2.0,Iris-virginica 6.3,2.7,4.9,1.8,Iris-virginica 6.7,3.3,5.7,2.1,Iris-virginica 7.2,3.2,6.0,1.8,Iris-virginica 6.2,2.8,4.8,1.8,Iris-virginica 6.1,3.0,4.9,1.8,Iris-virginica 6.4,2.8,5.6,2.1,Iris-virginica 7.2,3.0,5.8,1.6,Iris-virginica 7.4,2.8,6.1,1.9,Iris-virginica 7.9,3.8,6.4,2.0,Iris-virginica 6.4,2.8,5.6,2.2,Iris-virginica 6.3,2.8,5.1,1.5,Iris-virginica 6.1,2.6,5.6,1.4,Iris-virginica 7.7,3.0,6.1,2.3,Iris-virginica 6.3,3.4,5.6,2.4,Iris-virginica 6.4,3.1,5.5,1.8,Iris-virginica 6.0,3.0,4.8,1.8,Iris-virginica 6.9,3.1,5.4,2.1,Iris-virginica 6.7,3.1,5.6,2.4,Iris-virginica 6.9,3.1,5.1,2.3,Iris-virginica 5.8,2.7,5.1,1.9,Iris-virginica 6.8,3.2,5.9,2.3,Iris-virginica 6.7,3.3,5.7,2.5,Iris-virginica 6.7,3.0,5.2,2.3,Iris-virginica 6.3,2.5,5.0,1.9,Iris-virginica 6.5,3.0,5.2,2.0,Iris-virginica 6.2,3.4,5.4,2.3,Iris-virginica 5.9,3.0,5.1,1.8,Iris-virginica



The following keraslearn.py requires the additional installation of python module as below
pip install keras
python keraslearn.py    Select all#!/usr/bin/env python from keras.models import Sequential from keras.layers import Dense import numpy import time # fix random seed for reproducibility numpy.random.seed(7) # load pima indians dataset #dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") dataset = numpy.loadtxt("pima-indians-diabetes.data", delimiter=",") # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) start_time=time.time() # Fit the model #model.fit(X, Y, epochs=150, batch_size=10) model.fit(X, Y, 10, 150) # parameters change to keras 1.2.2 # evaluate the model scores = model.evaluate(X, Y) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) print("\nTraining took %.2f seconds\n" %(time.time()-start_time))



pima-indians-diabetes.data
pima-indians-diabetes.data    Select all 6,148,72,35,0,33.6,0.627,50,1 1,85,66,29,0,26.6,0.351,31,0 8,183,64,0,0,23.3,0.672,32,1 1,89,66,23,94,28.1,0.167,21,0 0,137,40,35,168,43.1,2.288,33,1 5,116,74,0,0,25.6,0.201,30,0 3,78,50,32,88,31.0,0.248,26,1 10,115,0,0,0,35.3,0.134,29,0 2,197,70,45,543,30.5,0.158,53,1 8,125,96,0,0,0.0,0.232,54,1 4,110,92,0,0,37.6,0.191,30,0 10,168,74,0,0,38.0,0.537,34,1 10,139,80,0,0,27.1,1.441,57,0 1,189,60,23,846,30.1,0.398,59,1 5,166,72,19,175,25.8,0.587,51,1 7,100,0,0,0,30.0,0.484,32,1 0,118,84,47,230,45.8,0.551,31,1 7,107,74,0,0,29.6,0.254,31,1 1,103,30,38,83,43.3,0.183,33,0 1,115,70,30,96,34.6,0.529,32,1 3,126,88,41,235,39.3,0.704,27,0 8,99,84,0,0,35.4,0.388,50,0 7,196,90,0,0,39.8,0.451,41,1 9,119,80,35,0,29.0,0.263,29,1 11,143,94,33,146,36.6,0.254,51,1 10,125,70,26,115,31.1,0.205,41,1 7,147,76,0,0,39.4,0.257,43,1 1,97,66,15,140,23.2,0.487,22,0 13,145,82,19,110,22.2,0.245,57,0 5,117,92,0,0,34.1,0.337,38,0 5,109,75,26,0,36.0,0.546,60,0 3,158,76,36,245,31.6,0.851,28,1 3,88,58,11,54,24.8,0.267,22,0 6,92,92,0,0,19.9,0.188,28,0 10,122,78,31,0,27.6,0.512,45,0 4,103,60,33,192,24.0,0.966,33,0 11,138,76,0,0,33.2,0.420,35,0 9,102,76,37,0,32.9,0.665,46,1 2,90,68,42,0,38.2,0.503,27,1 4,111,72,47,207,37.1,1.390,56,1 3,180,64,25,70,34.0,0.271,26,0 7,133,84,0,0,40.2,0.696,37,0 7,106,92,18,0,22.7,0.235,48,0 9,171,110,24,240,45.4,0.721,54,1 7,159,64,0,0,27.4,0.294,40,0 0,180,66,39,0,42.0,1.893,25,1 1,146,56,0,0,29.7,0.564,29,0 2,71,70,27,0,28.0,0.586,22,0 7,103,66,32,0,39.1,0.344,31,1 7,105,0,0,0,0.0,0.305,24,0 1,103,80,11,82,19.4,0.491,22,0 1,101,50,15,36,24.2,0.526,26,0 5,88,66,21,23,24.4,0.342,30,0 8,176,90,34,300,33.7,0.467,58,1 7,150,66,42,342,34.7,0.718,42,0 1,73,50,10,0,23.0,0.248,21,0 7,187,68,39,304,37.7,0.254,41,1 0,100,88,60,110,46.8,0.962,31,0 0,146,82,0,0,40.5,1.781,44,0 0,105,64,41,142,41.5,0.173,22,0 2,84,0,0,0,0.0,0.304,21,0 8,133,72,0,0,32.9,0.270,39,1 5,44,62,0,0,25.0,0.587,36,0 2,141,58,34,128,25.4,0.699,24,0 7,114,66,0,0,32.8,0.258,42,1 5,99,74,27,0,29.0,0.203,32,0 0,109,88,30,0,32.5,0.855,38,1 2,109,92,0,0,42.7,0.845,54,0 1,95,66,13,38,19.6,0.334,25,0 4,146,85,27,100,28.9,0.189,27,0 2,100,66,20,90,32.9,0.867,28,1 5,139,64,35,140,28.6,0.411,26,0 13,126,90,0,0,43.4,0.583,42,1 4,129,86,20,270,35.1,0.231,23,0 1,79,75,30,0,32.0,0.396,22,0 1,0,48,20,0,24.7,0.140,22,0 7,62,78,0,0,32.6,0.391,41,0 5,95,72,33,0,37.7,0.370,27,0 0,131,0,0,0,43.2,0.270,26,1 2,112,66,22,0,25.0,0.307,24,0 3,113,44,13,0,22.4,0.140,22,0 2,74,0,0,0,0.0,0.102,22,0 7,83,78,26,71,29.3,0.767,36,0 0,101,65,28,0,24.6,0.237,22,0 5,137,108,0,0,48.8,0.227,37,1 2,110,74,29,125,32.4,0.698,27,0 13,106,72,54,0,36.6,0.178,45,0 2,100,68,25,71,38.5,0.324,26,0 15,136,70,32,110,37.1,0.153,43,1 1,107,68,19,0,26.5,0.165,24,0 1,80,55,0,0,19.1,0.258,21,0 4,123,80,15,176,32.0,0.443,34,0 7,81,78,40,48,46.7,0.261,42,0 4,134,72,0,0,23.8,0.277,60,1 2,142,82,18,64,24.7,0.761,21,0 6,144,72,27,228,33.9,0.255,40,0 2,92,62,28,0,31.6,0.130,24,0 1,71,48,18,76,20.4,0.323,22,0 6,93,50,30,64,28.7,0.356,23,0 1,122,90,51,220,49.7,0.325,31,1 1,163,72,0,0,39.0,1.222,33,1 1,151,60,0,0,26.1,0.179,22,0 0,125,96,0,0,22.5,0.262,21,0 1,81,72,18,40,26.6,0.283,24,0 2,85,65,0,0,39.6,0.930,27,0 1,126,56,29,152,28.7,0.801,21,0 1,96,122,0,0,22.4,0.207,27,0 4,144,58,28,140,29.5,0.287,37,0 3,83,58,31,18,34.3,0.336,25,0 0,95,85,25,36,37.4,0.247,24,1 3,171,72,33,135,33.3,0.199,24,1 8,155,62,26,495,34.0,0.543,46,1 1,89,76,34,37,31.2,0.192,23,0 4,76,62,0,0,34.0,0.391,25,0 7,160,54,32,175,30.5,0.588,39,1 4,146,92,0,0,31.2,0.539,61,1 5,124,74,0,0,34.0,0.220,38,1 5,78,48,0,0,33.7,0.654,25,0 4,97,60,23,0,28.2,0.443,22,0 4,99,76,15,51,23.2,0.223,21,0 0,162,76,56,100,53.2,0.759,25,1 6,111,64,39,0,34.2,0.260,24,0 2,107,74,30,100,33.6,0.404,23,0 5,132,80,0,0,26.8,0.186,69,0 0,113,76,0,0,33.3,0.278,23,1 1,88,30,42,99,55.0,0.496,26,1 3,120,70,30,135,42.9,0.452,30,0 1,118,58,36,94,33.3,0.261,23,0 1,117,88,24,145,34.5,0.403,40,1 0,105,84,0,0,27.9,0.741,62,1 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1,91,54,25,100,25.2,0.234,23,0 1,117,60,23,106,33.8,0.466,27,0 5,123,74,40,77,34.1,0.269,28,0 2,120,54,0,0,26.8,0.455,27,0 1,106,70,28,135,34.2,0.142,22,0 2,155,52,27,540,38.7,0.240,25,1 2,101,58,35,90,21.8,0.155,22,0 1,120,80,48,200,38.9,1.162,41,0 11,127,106,0,0,39.0,0.190,51,0 3,80,82,31,70,34.2,1.292,27,1 10,162,84,0,0,27.7,0.182,54,0 1,199,76,43,0,42.9,1.394,22,1 8,167,106,46,231,37.6,0.165,43,1 9,145,80,46,130,37.9,0.637,40,1 6,115,60,39,0,33.7,0.245,40,1 1,112,80,45,132,34.8,0.217,24,0 4,145,82,18,0,32.5,0.235,70,1 10,111,70,27,0,27.5,0.141,40,1 6,98,58,33,190,34.0,0.430,43,0 9,154,78,30,100,30.9,0.164,45,0 6,165,68,26,168,33.6,0.631,49,0 1,99,58,10,0,25.4,0.551,21,0 10,68,106,23,49,35.5,0.285,47,0 3,123,100,35,240,57.3,0.880,22,0 8,91,82,0,0,35.6,0.587,68,0 6,195,70,0,0,30.9,0.328,31,1 9,156,86,0,0,24.8,0.230,53,1 0,93,60,0,0,35.3,0.263,25,0 3,121,52,0,0,36.0,0.127,25,1 2,101,58,17,265,24.2,0.614,23,0 2,56,56,28,45,24.2,0.332,22,0 0,162,76,36,0,49.6,0.364,26,1 0,95,64,39,105,44.6,0.366,22,0 4,125,80,0,0,32.3,0.536,27,1 5,136,82,0,0,0.0,0.640,69,0 2,129,74,26,205,33.2,0.591,25,0 3,130,64,0,0,23.1,0.314,22,0 1,107,50,19,0,28.3,0.181,29,0 1,140,74,26,180,24.1,0.828,23,0 1,144,82,46,180,46.1,0.335,46,1 8,107,80,0,0,24.6,0.856,34,0 13,158,114,0,0,42.3,0.257,44,1 2,121,70,32,95,39.1,0.886,23,0 7,129,68,49,125,38.5,0.439,43,1 2,90,60,0,0,23.5,0.191,25,0 7,142,90,24,480,30.4,0.128,43,1 3,169,74,19,125,29.9,0.268,31,1 0,99,0,0,0,25.0,0.253,22,0 4,127,88,11,155,34.5,0.598,28,0 4,118,70,0,0,44.5,0.904,26,0 2,122,76,27,200,35.9,0.483,26,0 6,125,78,31,0,27.6,0.565,49,1 1,168,88,29,0,35.0,0.905,52,1 2,129,0,0,0,38.5,0.304,41,0 4,110,76,20,100,28.4,0.118,27,0 6,80,80,36,0,39.8,0.177,28,0 10,115,0,0,0,0.0,0.261,30,1 2,127,46,21,335,34.4,0.176,22,0 9,164,78,0,0,32.8,0.148,45,1 2,93,64,32,160,38.0,0.674,23,1 3,158,64,13,387,31.2,0.295,24,0 5,126,78,27,22,29.6,0.439,40,0 10,129,62,36,0,41.2,0.441,38,1 0,134,58,20,291,26.4,0.352,21,0 3,102,74,0,0,29.5,0.121,32,0 7,187,50,33,392,33.9,0.826,34,1 3,173,78,39,185,33.8,0.970,31,1 10,94,72,18,0,23.1,0.595,56,0 1,108,60,46,178,35.5,0.415,24,0 5,97,76,27,0,35.6,0.378,52,1 4,83,86,19,0,29.3,0.317,34,0 1,114,66,36,200,38.1,0.289,21,0 1,149,68,29,127,29.3,0.349,42,1 5,117,86,30,105,39.1,0.251,42,0 1,111,94,0,0,32.8,0.265,45,0 4,112,78,40,0,39.4,0.236,38,0 1,116,78,29,180,36.1,0.496,25,0 0,141,84,26,0,32.4,0.433,22,0 2,175,88,0,0,22.9,0.326,22,0 2,92,52,0,0,30.1,0.141,22,0 3,130,78,23,79,28.4,0.323,34,1 8,120,86,0,0,28.4,0.259,22,1 2,174,88,37,120,44.5,0.646,24,1 2,106,56,27,165,29.0,0.426,22,0 2,105,75,0,0,23.3,0.560,53,0 4,95,60,32,0,35.4,0.284,28,0 0,126,86,27,120,27.4,0.515,21,0 8,65,72,23,0,32.0,0.600,42,0 2,99,60,17,160,36.6,0.453,21,0 1,102,74,0,0,39.5,0.293,42,1 11,120,80,37,150,42.3,0.785,48,1 3,102,44,20,94,30.8,0.400,26,0 1,109,58,18,116,28.5,0.219,22,0 9,140,94,0,0,32.7,0.734,45,1 13,153,88,37,140,40.6,1.174,39,0 12,100,84,33,105,30.0,0.488,46,0 1,147,94,41,0,49.3,0.358,27,1 1,81,74,41,57,46.3,1.096,32,0 3,187,70,22,200,36.4,0.408,36,1 6,162,62,0,0,24.3,0.178,50,1 4,136,70,0,0,31.2,1.182,22,1 1,121,78,39,74,39.0,0.261,28,0 3,108,62,24,0,26.0,0.223,25,0 0,181,88,44,510,43.3,0.222,26,1 8,154,78,32,0,32.4,0.443,45,1 1,128,88,39,110,36.5,1.057,37,1 7,137,90,41,0,32.0,0.391,39,0 0,123,72,0,0,36.3,0.258,52,1 1,106,76,0,0,37.5,0.197,26,0 6,190,92,0,0,35.5,0.278,66,1 2,88,58,26,16,28.4,0.766,22,0 9,170,74,31,0,44.0,0.403,43,1 9,89,62,0,0,22.5,0.142,33,0 10,101,76,48,180,32.9,0.171,63,0 2,122,70,27,0,36.8,0.340,27,0 5,121,72,23,112,26.2,0.245,30,0 1,126,60,0,0,30.1,0.349,47,1 1,93,70,31,0,30.4,0.315,23,0



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