def predict():
X = [item for item in request.form.values()]
X = [float(X[i]) if X[i].isnumeric() else np.nan for i in range(8) ] + [str(X[i]) for i in range(8, 15)]
X = pd.DataFrame(data=[X], columns=features)
item = data.head(1)
for i in features:
item[i] = X[i]
final_features = full_pipeline.transform(item)
prediction_svm = convert(SVM_Grid.best_estimator_.predict(final_features))
prediction_knn = convert(KNN_Grid.best_estimator_.predict(final_features))
prediction_rf = convert(RF_Random.predict(final_features))
return render_template('index.html', prediction_svm="(SVM) House's Price should be {}".format(prediction_svm), prediction_knn="(KNN) House's Price should be {}".format(prediction_knn), prediction_rf="(RF) House's Price should be {}".format(prediction_rf) )
def pretty_print(maze):
print("")
for a in m.convert(maze):
string = ""
for b in a:
string += str(b)
print(string)
print("")
def get_prediction(payload: StockIn):
ticker = payload.ticker
prediction_list = predict(ticker)
if not prediction_list:
raise HTTPException(status_code=400, detail="Model not found.")
response_object = {"ticker": ticker, "forecast": convert(prediction_list)}
return response_object
def test_find_end_point(self):
data = model.convert(model.make_empty_maze(10, 10))
hasEndpoint = False
for a in data:
for b in a:
if b == 2:
hasEndpoint = True
# print(hasEndpoint)
# print(data)
self.assertTrue(hasEndpoint, "have found two")
def auth():
email = request.args.get('email')
password = request.args.get('password')
result = model.authenticate(email, password)
if isinstance(result, model.User):
return jsonify(model.convert(result))
else:
return abort(401)
def do_dffs(dffs, in_models, dffpath, targetpath):
print("*** Processing %d DFF's ***" % len(dffs))
for dfffile in dffs:
writepath = targetpath + dfffile + ".js"
if not overwrite and os.path.exists(writepath):
continue
txdpath = ""
if dfffile.lower() in in_models:
txdpath = "data/textures/"+in_models[dfffile.lower()]["TextureName"]+"/"
dff = model.convert(dffpath+dfffile.lower()+".dff", txdpath)
f = open(writepath, "w")
json.dump(dff.data, f)
f.close()
def solve_multiple_mazes(solution_alg, loop):
maze_data = {}
for size in range(5, 35, 5):
maze_data[size] = {}
empty_maze = m.make_empty_maze(size, size)
maze = m.DFS(empty_maze)
converted_maze = m.convert(maze)
#m.save_maze("maze.csv", converted_maze)
#loaded_maze = m.read_maze("maze.csv")
times, moves = solve_with_algorithm(solution_alg, converted_maze, loop)
maze_data[size]["moves"] = moves[0]
maze_data[size]["avg_time"] = m.calc_average(times)
maze_data[size]["min_time"] = min(times)
maze_data[size]["max_time"] = max(times)
view.print_result(maze_data, size)
#view.print_times(times)
return maze_data
def solveMaze(size):
grid = model.convert(model.DFS(model.make_empty_maze(size, size)))
start = time.time()
model.search(1, 1, grid)
end = time.time()
#print(grid)
count = 0
flattened_list = [y for x in grid for y in x]
for n in flattened_list:
if n == 3 or n == 2:
count += 1
t = end - start
lis = [t, count]
return lis
def csvPrint():
grid = model.convert(model.DFS(model.make_empty_maze(5, 5)))
printer.printFile(grid)
#model.search(1, 1, grid)
printer.readFile()
from model import train, predict, convert ticker = "AAPL" train(ticker) prediction_list = predict(ticker) result = convert(prediction_list) print(result)
from model import train, predict, convert
train()
prediction_list = predict()
convert(prediction_list)
train("GOOG")
train("AAPL")
train("^GSPC")
$ conda create -n env python=3.8
conda activate env (To deactivate use - conda deactivate env)
$ pip install -r requirements.txt
$ python
>>> from model import train, predict, convert
>>> train()
>>> prediction_list = predict()
>>> convert(prediction_list)
train("FB")
train("AAPL")
train("AMZN")
train("NFLX")
train("GOOG")
train("TSLA")