def main(): print("PCA") pca.pca() print("RandomForest") rf.rf(2) print("KNN") knn.knn(2) print("SVC") svc.svc() print("GRID_SVC") svc.gridSearchScore() print("Logistic") logistic.Logistic().fit() print("DNN Classifier") classifier_model = classifier.classifier() classifier_model.fit()
""" random forest for forest types dataset """ import pandas as pd import numpy as np from sklearn import cross_validation from rf import rf # custom function (in rf.py) for random forests import os os.chdir("/Users/sandy/work/UF/ML/Project/code/RandomForests") """ training.csv """ train = pd.read_csv("../../datasets/ForestTypes/training.csv") features = train.columns[1:] X_train = train[features] X_train = X_train.astype(float) y_train = train['class'] """ testing.csv """ test = pd.read_csv("../../datasets/ForestTypes/testing.csv") features = test.columns[1:] X_test = test[features] X_test = X_test.astype(float) y_test = test['class'] print rf(X_train, X_test, y_train, y_test, "Forest types")
import os os.chdir("/Users/sandy/work/UF/ML/Project/code/RandomForests") """ breast-cancer-wisconsin.data """ data = pd.read_csv( "../../datasets/breast-cancer-wisconsin/breast-cancer-wisconsin.data", header=None) data = data.replace('?', np.nan).astype(float, raise_on_error=False).dropna( how='any') # removing missing values num_columns = len(data.columns) features = data.columns[1:-1] X = data[features] y = data[num_columns - 1] X_train, X_test, y_train, y_test = cross_validation.train_test_split( X, y, test_size=0.1, random_state=0) print rf(X_train, X_test, y_train, y_test, "Wisconsin Breast Cancer Database") """ wdbc.data """ data = pd.read_csv("../../datasets/breast-cancer-wisconsin/wdbc.data", header=None) data = data.astype(float, raise_on_error=False).dropna( how='any') # removing missing values features = data.columns[2:] X = data[features] y = data[1] X_train, X_test, y_train, y_test = cross_validation.train_test_split( X, y, test_size=0.1, random_state=0) print rf(X_train, X_test, y_train, y_test, "Wisconsin Diagnostic Breast Cancer") """ wpbc.data """ data = pd.read_csv("../../datasets/breast-cancer-wisconsin/wpbc.data", header=None)
""" random forest for forest types dataset """ import pandas as pd import numpy as np from sklearn import cross_validation from rf import rf # custom function (in rf.py) for random forests import os os.chdir("/Users/sandy/work/UF/ML/Project/code/RandomForests") """ training.csv """ train = pd.read_csv("../../datasets/ForestTypes/training.csv") features = train.columns[1:] X_train = train[features] X_train = X_train.astype(float) y_train = train['class'] """ testing.csv """ test = pd.read_csv("../../datasets/ForestTypes/testing.csv") features = test.columns[1:] X_test = test[features] X_test = X_test.astype(float) y_test = test['class'] print rf(X_train, X_test, y_train, y_test,"Forest types")
from metric import printErrorMetrics from rf import rf from rr import rr from nn import nn from lr import lr if __name__ == '__main__': extract_dir = sys.argv[1] fnum = int(sys.argv[2]) """ datasets and labels's size is fnum """ datasets, labels = GetAllData(extract_dir, fnum, 'bfs', total_vertex_num=4900578, L=500000) # datasets, labels = GetAllData(extract_dir, fnum, 'bfs', total_vertex_num=65608366, L=10000000) """ ridge regression """ sr, sl = rr(datasets, labels, fnum) """ neural network """ sr, sl = nn(datasets, labels, fnum) """ liner regression """ sr, sl = lr(datasets, labels, fnum) """ random forest """ sr, sl = rf(datasets, labels, fnum) """ draw picture """ sample_draw(sr,sl)
import rf # First get data by running this command in the console # obspyDMT --identity 'TA.Z30A.*.BHZ' --min_mag '5.8' --min_date '2011-01-01' --max_date '2011-01-10' --event_catalog 'IRIS' --arc 'N' # Set data path of obspyDMT here or in conf.py rf.set_paths('~/obspyDMT-data/2011-01-01_2011-01-10_5.8_9.9') # Convert pickled events to catalog file events.xml rf.convert_dmteventfile() # Select events for RF rf.create_rfeventsfile(filters=[]) # Calculate RFs. They will be in the RF directory of the corresponding event rf.rf('dmt', downsample=10)
if int(rec0) == 1: uid = input('which user you want to apply(0~3065)\n-->') uid = int(input('which user you want to apply(0~3065)\n-->')) n = int(input('how many item you want to recommandat, as int\n-->')) if int(algo) == 3: fac = int(input('how many factors you want, as int\n-->')) result = svdrec(factors=fac) caculate_mse(result) if int(cm) == 1: drawcm(result, title='MF') if int(rec0) == 1: uid = int(input('which user you want to apply(0~3065)\n-->')) n = int(input('how many item you want to recommandat, as int\n-->')) rec(result, uid, n, rawId=True) if int(algo) == 4: result = cf() caculate_mse(result) if int(cm) == 1: drawcm(result, title='MF') if int(rec0) == 1: uid = int(input('which user you want to apply(0~3065)\n-->')) n = int(input('how many item you want to recommandat, as int\n-->')) rec(result, uid, n, rawId=True) if int(algo) == 5: a = rf()
""" random forest for handwritten digits dataset """ import pandas as pd import numpy as np from sklearn import cross_validation from rf import rf # custom function (in rf.py) for random forests import os os.chdir("/Users/sandy/work/UF/ML/Project/code/RandomForests") """ optdigits.tra """ train = pd.read_csv("../../datasets/optdigits/optdigits.tra", header=None) num_columns = len(train.columns) features = train.columns[:-1] X_train = train[features] y_train = train[num_columns - 1] """ optdigits.tes """ test = pd.read_csv("../../datasets/optdigits/optdigits.tes", header=None) num_columns = len(train.columns) features = test.columns[:-1] X_test = test[features] y_test = test[num_columns - 1] print rf(X_train, X_test, y_train, y_test, "Optical Recognition of Handwritten Digits")
for c in cuenta1: valor1 = c.cuenta for c in cuenta2: valor2 = c.cuenta if valor1 < valor2: mini = valor1 else: mini = valor2 if mini> nnodes: mini = nnodes/2 nodes1 = neo4j.CypherQuery(graph_db, "match (n:"+tipo+") where n."+target+" <> '"+vtarget+ "' return n,id(n) as id,n."+target+" as "+target+" LIMIT "+str(mini)+" UNION ALL "+"match (n:"+tipo+") where n."+target+" = '"+vtarget+ "' return n,id(n) as id,n."+target+" as "+target+" LIMIT "+str(mini)).execute() print "Conjunto de nodos cargado: "+str(len(nodes1.data))+" elementos." nodestrain = [] nodestest = [] for z in nodes1: if random.randint(0,5)>1: nodestrain.append(z) else: nodestest.append(z) print "Conjunto de entrenamiento: "+str(len(nodestrain))+" elementos." print "Conjunto de prueba: "+str(len(nodestest))+" elementos." #graph, tipo, target, vtarget, narboles, nnodos, nrels, maxdepth, exrels,umbral rf = rf(graph_db,nodestrain,tipo,target,vtarget,1,nnodes,100,2,[],0 ) rf.train() rf.test(nodestest) np.save("rf2", rf)
a_g, trace, dict_of_all_sp_grt, initial_sp_c, grt_dict, stoichiometric_dict, switch, temperature, v=v, pressure=pressure) # ========================================================================= # Release fraction module # ========================================================================= species_updated = sp_g + sp_c rf.rf(y, species_updated, input1, stoichiometric_dict, el_inventory) elif method == 'SLSQP': a = np.zeros([len(input1), len(species)]) x0 = np.ones(len(species)) * 0.1 k1 = 0 for j in species: a[:, k1] = stoichiometric_coeff_matrix_generator.stoi( j, input1, stoichiometric_dict) k1 = k1 + 1 no_it = 20 opt1 = { 'eps': 1e-3, 'maxiter': 20000, 'ftol': 1e-6,
from sklearn import cross_validation from rf import rf # custom function (in rf.py) for random forests import os os.chdir("/Users/sandy/work/UF/ML/Project/code/RandomForests") """ breast-cancer-wisconsin.data """ data = pd.read_csv("../../datasets/breast-cancer-wisconsin/breast-cancer-wisconsin.data",header=None) data = data.replace('?',np.nan).astype(float,raise_on_error=False).dropna(how='any') # removing missing values num_columns = len(data.columns) features = data.columns[1:-1] X = data[features] y = data[num_columns-1] X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y,test_size=0.1,random_state=0) print rf(X_train, X_test, y_train, y_test,"Wisconsin Breast Cancer Database") """ wdbc.data """ data = pd.read_csv("../../datasets/breast-cancer-wisconsin/wdbc.data",header=None) data = data.astype(float,raise_on_error=False).dropna(how='any') # removing missing values features = data.columns[2:] X = data[features] y = data[1] X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y,test_size=0.1,random_state=0) print rf(X_train, X_test, y_train, y_test,"Wisconsin Diagnostic Breast Cancer") """ wpbc.data """ data = pd.read_csv("../../datasets/breast-cancer-wisconsin/wpbc.data",header=None) data = data.replace('?',np.nan).astype(float,raise_on_error=False).dropna(how='any') # removing missing values features = data.columns[3:] X = data[features]
""" random forest for handwritten digits dataset """ import pandas as pd import numpy as np from sklearn import cross_validation from rf import rf # custom function (in rf.py) for random forests import os os.chdir("/Users/sandy/work/UF/ML/Project/code/RandomForests") """ optdigits.tra """ train = pd.read_csv("../../datasets/optdigits/optdigits.tra",header=None) num_columns = len(train.columns) features = train.columns[:-1] X_train = train[features] y_train = train[num_columns-1] """ optdigits.tes """ test = pd.read_csv("../../datasets/optdigits/optdigits.tes",header=None) num_columns = len(train.columns) features = test.columns[:-1] X_test = test[features] y_test = test[num_columns-1] print rf(X_train, X_test, y_train, y_test,"Optical Recognition of Handwritten Digits")
print(x) print(y) print(z) x, y, z = svm() print(x) print(y) print(z) x, y, z = nb() print(x) print(y) print(z) x, y, z = dt() print(x) print(y) print(z) x, y, z = rf() print(x) print(y) print(z) x, y, z = gbm() print(x) print(y) print(z) x, y, z = knn() print(x) print(y) print(z) x, y, z = ada() print(x) print(y) print(z)
import obspy.iris import rf # Create getwaveform function which has to be passed to rf function client = obspy.iris.Client() def getwaveform(station, t1, t2): return client.getWaveform('TA', station, '', 'BH?', t1, t2) # Set output path here or in config file rf.set_paths('~/obspyDMT-data/client_test') # Create event file from given events rf.create_rfeventsfile('./events.xml') #Calculate receiver functions. Station coordinates are given in stations.txt rf.rf('client', getwaveform, './stations.txt', deconvolve='freq')