def test_visualize(self):
model_dir = os.path.abspath('../data/model/20160620-173927')
create_checkpoint_file(model_dir, 'model.ckpt-500000')
argv = [model_dir,
'--model_def', 'models.nn4' ]
args = visualize.parse_arguments(argv)
visualize.main(args)
def test_visualize(self):
model_dir = os.path.abspath('../data/model/20160620-173927')
create_checkpoint_file(model_dir, 'model.ckpt-500000')
argv = [model_dir,
'--model_def', 'models.nn4' ]
args = visualize.parse_arguments(argv)
visualize.main(args)
def main():
if len(sys.argv) <= 1:
print("ERROR: Missing Arguments")
printHelp()
elif sys.argv[1] == "test":
print("Flask Tests")
suite = unittest.TestLoader().loadTestsFromModule(flask_tests)
unittest.TextTestRunner(verbosity=0).run(suite)
print("Module Tests")
suite = unittest.TestLoader().loadTestsFromModule(module_tests)
unittest.TextTestRunner(verbosity=0).run(suite)
print("Inference Tests")
suite = unittest.TestLoader().loadTestsFromModule(inference_tests)
unittest.TextTestRunner(verbosity=0).run(suite)
elif sys.argv[1] == "run":
application.run()
elif sys.argv[1] == "data":
visualize.main()
else:
print("ERROR: Invalid Arguments")
printHelp()
import gmplot
if len(sys.argv) < 2:
print "Usage: python", sys.argv[0], "mode"
sys.exit()
mode = int(sys.argv[1])
# mode 1 draws the scatter plot the old way from training data
if mode == 1:
with open("category_list.txt") as f:
category_list = f.read().splitlines()
for i in category_list:
print (i)
for year in range(2003, 2015 + 1):
visualize.main("train.csv", i, year, draw_heatmap=False)
# mode 2 takes a csv file(col0=timestamp, col1=lat, col2=lon)
# and draws a scatter plot
elif mode == 2:
fname = sys.argv[2]
csvfile = open(fname)
csv = csv.reader(csvfile, delimiter=",")
xlist = []
ylist = []
for row in csv:
timestamp = float(row[0])
lat = float(row[1])
lon = float(row[2])
xlist.append(lat)
def test_main_runs(self):
self.assertTrue(main())
def get_columns(in_file): #,reg_type):
try:
data = pd.read_csv(in_file, encoding='latin1')
except:
sys.exit(
colored('File ' + str(in_file) +
' does not seem to be a valid CSV file',
'red',
attrs=['bold']))
#selecting only numeric columns in selected file
dataint = data[data.columns[data.dtypes == 'int64']]
datafloat = data[data.columns[data.dtypes == 'float64']]
#setting a dummy name for row index so the data can be merged based on this index
data.index.name = 'dummy_index'
data = pd.merge(dataint, datafloat, on='dummy_index')
columns = data.columns
print(
colored('Numeric columns in file %s' % (in_file),
'blue',
attrs=['bold']))
for i in range(len(columns)):
#+1 so user does not need to type in 0
print(
colored(str(i + 1) + '-' + columns[i] + ' ' +
str(data[columns[i]].dtypes),
'green',
attrs=['bold']))
inp = ''
chosen_columns = []
while (inp != 'q'):
inp = input(
colored(
'Choose one column and press Enter or type in \'0\' to finish selecting. (Type in \'a\' to choose all columns at once) ',
'white',
attrs=['bold']))
if (inp == 'a'):
chosen_columns = list(columns)
break
elif (int(inp) == 0):
break
elif (int(inp) in range(1, len(columns) + 1)):
data[columns[int(inp) - 1]]
chosen_columns.append(columns[int(inp) - 1])
else:
print(
colored('Invalid column. Please, select a valid column name ',
'red',
attrs=['bold']))
selected_data = data[chosen_columns]
#Prompting user for what kind of regression to perform
mod = ''
print(colored("1 - Logistic\n2 - Linear", 'green', attrs=['bold']))
mod = int(
input(
colored("Which type of regression do you want to perform? ",
'white',
attrs=['bold'])))
while (1 > 0):
if (mod == 1):
reg_type = 'logistic'
break
elif (mod == 2):
reg_type = 'linear'
break
else:
print(
colored(
"Invalid option. Please, select [1] for logistic regression or [2] for linear regression ",
'red',
attrs=['underline', 'bold']))
mod = int(
input(
colored(
"Which type of regression do you want to perform? ",
'cyan',
attrs=['bold'])))
check_data(selected_data, reg_type)
print(colored("Chosen columns:", 'blue', attrs=['bold']))
for i in range(len(chosen_columns)):
print(
colored(str(i + 1) + '-' + chosen_columns[i],
'green',
attrs=['bold']))
#asks the user for the dependent variable column
target = int(
input(colored('Choose the target column ', 'white', attrs=['bold'])))
tar = selected_data[chosen_columns[target - 1]]
target_name = chosen_columns[target - 1]
print(
colored("You chose '" + str(chosen_columns[target - 1]) +
"' as your target column ",
'yellow',
attrs=['bold']))
#removing target column from the chosen columns list, so the function can return x and y arrays separately
chosen_columns.pop(target - 1)
final_data = pd.DataFrame(selected_data[chosen_columns])
check_data(selected_data, reg_type)
#plotting features against target column
plo = ''
plo = str(
input(
colored(
"Do you want to visualize your data before analyzing it? [y/n]",
'white',
attrs=['bold'])))
#mod = int(input(colored("Which type of regression do you want to perform? ",'white',attrs=['bold'])))
while (1 > 0):
if (plo == 'y'):
vi.main(final_data, tar)
break
elif (plo == 'n'):
#reg_type = 'linear'
break
else:
print(
colored("Invalid option. Please, select y or n ",
'red',
attrs=['underline', 'bold']))
plo = str(
input(
colored(
"Do you want to visualize your data before analyzing it? [y/n] ",
'cyan',
attrs=['bold'])))
#adding bias column to x array
add_bias(final_data)
chosen_columns.insert(0, 'bias')
return np.array(final_data), np.array(
tar), reg_type, chosen_columns, target_name
try:
pygame.display.quit()
except:
return
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
try:
solve()
except:
running = False
running = True
pygame.init()
pygame.display.init()
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
visualize.main()
with open("solution.txt", "w") as file:
file.write("")
quit()
v = np.full((n, 1),
.001) #each point will assign velocities to each of those points
#v = np.random.uniform(0,0.01,n).reshape(-1,1)
#each point should be assigned two other points at random
rows_v12 = np.apply_along_axis(other_vertex_rows, 1,
np.arange(n).reshape(-1, 1))
coords = tc.triangle_coords(
n, p0, rows_v12
) #instantiation of class for calculating point positions, and initialization of positions for each point
#coords.step(dist_p0=v, personal_space=personal_space) #during each generation each point moves by dist_p0 with the objective of forming an equilateral triangle
visualize.main(
triangle_coords=coords,
dist_p0=v,
personal_space=personal_space,
plt_type=plt_type
) #visualize points moving through successive calls of the coords.step() method
'''
#Functionality to add:
#points should be constrained such that their initial positions do not overlap or collide (i.e., no closer than a certain margin away from each other)
#better visualization of triangles created by each triple (p0,p1,p2), perhaps overlapping with points
#e.g., perhaps color-coding triangles according to whether they have reached equilateral shape
#implement an option in step() method for a different objective, e.g.: step(objective)
#..whereby the objective is for a point to move to a position where it is equidistant to each of the other two points
#..therefore it should move to the closest position between itself and the maximal margin hyperspace between the other two points
