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I want to add more layers in neural network how can I customize this code ? can any one suggest a better way
in model layer there is only one layer .. in this part , I can define other weights ?should I add other layers there but it would make no sense
MODEL_TYPE = tff.StructType([
('weights', tff.TensorType(tf.float32, [784, 10])),
('bias', tff.TensorType(tf.float32, [10]))])
then in batch loss function they multiply by them in order to get the predicted y.. that's simply adding one layer..
tff.tf_computation(MODEL_TYPE, BATCH_TYPE)
def batch_loss(model, batch):
predicted_y = tf.nn.softmax(tf.matmul(batch.x, model.weights) + model.bias)
The full code is here
from __future__ import absolute_import, division, print_function
import tensorflow_federated as tff
import tensorflow.compat.v1 as tf
import numpy as np
import time
from scipy.special import comb, perm
import collections
import os
tff.backends.reference.set_reference_context()
# tf.compat.v1.enable_v2_behavior()
# tf.compat.v1.enable_eager_execution()
# NUM_EXAMPLES_PER_USER = 1000
BATCH_SIZE = 100
NUM_AGENT = 5
DECAY_FACTOR = 0.8
def get_data_for_digit(source, digit):
output_sequence = []
all_samples = [i for i, d in enumerate(source[1]) if d == digit]
for i in range(0, len(all_samples), BATCH_SIZE):
batch_samples = all_samples[i:i + BATCH_SIZE]
output_sequence.append({
'x': np.array([source[0][i].flatten() / 255.0 for i in batch_samples],
dtype=np.float32),
'y': np.array([source[1][i] for i in batch_samples], dtype=np.int32)})
return output_sequence
def get_data_for_digit_test(source, digit):
output_sequence = []
all_samples = [i for i, d in enumerate(source[1]) if d == digit]
for i in range(0, len(all_samples)):
output_sequence.append({
'x': np.array(source[0][all_samples[i]].flatten() / 255.0,
dtype=np.float32),
'y': np.array(source[1][all_samples[i]], dtype=np.int32)})
return output_sequence
def get_data_for_federated_agents(source, num):
output_sequence = []
Samples = []
for digit in range(0, 10):
samples = [i for i, d in enumerate(source[1]) if d == digit]
samples = samples[0:5421]
Samples.append(samples)
all_samples = []
for sample in Samples:
for sample_index in range(int(num * (len(sample) / NUM_AGENT)), int((num + 1) * (len(sample) / NUM_AGENT))):
all_samples.append(sample[sample_index])
# all_samples = [i for i in range(int(num*(len(source[1])/NUM_AGENT)), int((num+1)*(len(source[1])/NUM_AGENT)))]
for i in range(0, len(all_samples), BATCH_SIZE):
batch_samples = all_samples[i:i + BATCH_SIZE]
output_sequence.append({
'x': np.array([source[0][i].flatten() / 255.0 for i in batch_samples],
dtype=np.float32),
'y': np.array([source[1][i] for i in batch_samples], dtype=np.int32)})
return output_sequence
BATCH_TYPE = tff.StructType([
('x', tff.TensorType(tf.float32, [None, 784])),
('y', tff.TensorType(tf.int32, [None]))])
MODEL_TYPE = tff.StructType([
('weights', tff.TensorType(tf.float32, [784, 10])),
('bias', tff.TensorType(tf.float32, [10]))])
#tff.tf_computation(MODEL_TYPE, BATCH_TYPE)
def batch_loss(model, batch):
predicted_y = tf.nn.softmax(tf.matmul(batch.x, model.weights) + model.bias)
return -tf.reduce_mean(tf.reduce_sum(
tf.one_hot(batch.y, 10) * tf.log(predicted_y), axis=[1]))
#tff.tf_computation(MODEL_TYPE, BATCH_TYPE, tf.float32)
def batch_train(initial_model, batch, learning_rate):
# Define a group of model variables and set them to `initial_model`.
model_vars = tff.utils.create_variables('v', MODEL_TYPE)
init_model = tff.utils.assign(model_vars, initial_model)
# Perform one step of gradient descent using loss from `batch_loss`.
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
with tf.control_dependencies([init_model]):
train_model = optimizer.minimize(batch_loss(model_vars, batch))
# Return the model vars after performing this gradient descent step.
with tf.control_dependencies([train_model]):
return tff.utils.identity(model_vars)
LOCAL_DATA_TYPE = tff.SequenceType(BATCH_TYPE)
#tff.federated_computation(MODEL_TYPE, tf.float32, LOCAL_DATA_TYPE)
def local_train(initial_model, learning_rate, all_batches):
# Mapping function to apply to each batch.
#tff.federated_computation(MODEL_TYPE, BATCH_TYPE)
def batch_fn(model, batch):
return batch_train(model, batch, learning_rate)
l = tff.sequence_reduce(all_batches, initial_model, batch_fn)
return l
#tff.federated_computation(MODEL_TYPE, LOCAL_DATA_TYPE)
def local_eval(model, all_batches):
#
return tff.sequence_sum(
tff.sequence_map(
tff.federated_computation(
lambda b: batch_loss(model, b), BATCH_TYPE),
all_batches))
SERVER_MODEL_TYPE = tff.FederatedType(MODEL_TYPE, tff.SERVER, all_equal=True)
CLIENT_DATA_TYPE = tff.FederatedType(LOCAL_DATA_TYPE, tff.CLIENTS)
#tff.federated_computation(SERVER_MODEL_TYPE, CLIENT_DATA_TYPE)
def federated_eval(model, data):
return tff.federated_mean(
tff.federated_map(local_eval, [tff.federated_broadcast(model), data]))
SERVER_FLOAT_TYPE = tff.FederatedType(tf.float32, tff.SERVER, all_equal=True)
#tff.federated_computation(
SERVER_MODEL_TYPE, SERVER_FLOAT_TYPE, CLIENT_DATA_TYPE)
def federated_train(model, learning_rate, data):
l = tff.federated_map(
local_train,
[tff.federated_broadcast(model),
tff.federated_broadcast(learning_rate),
data])
return l
# return tff.federated_mean()
def readTestImagesFromFile(distr_same):
ret = []
if distr_same:
f = open(os.path.join(os.path.dirname(__file__),
"test_images1_.txt"), encoding="utf-8")
else:
f = open(os.path.join(os.path.dirname(__file__),
"test_images1_.txt"), encoding="utf-8")
lines = f.readlines()
for line in lines:
tem_ret = []
p = line.replace("[", "").replace("]", "").replace(
"\n", "").split("\t")
for i in p:
if i != "":
tem_ret.append(float(i))
ret.append(tem_ret)
return np.asarray(ret)
def readTestLabelsFromFile(distr_same):
ret = []
if distr_same:
f = open(os.path.join(os.path.dirname(__file__),
"test_labels_.txt"), encoding="utf-8")
else:
f = open(os.path.join(os.path.dirname(__file__),
"test_labels_.txt"), encoding="utf-8")
lines = f.readlines()
for line in lines:
tem_ret = []
p = line.replace("[", "").replace("]", "").replace("\n", "").split(" ")
for i in p:
if i != "":
tem_ret.append(float(i))
ret.append(tem_ret)
return np.asarray(ret)
def getParmsAndLearningRate(agent_no):
f = open(os.path.join(os.path.dirname(__file__),
"weights_" + str(agent_no) + ".txt"))
content = f.read()
g_ = content.split(
"***\n--------------------------------------------------")
parm_local = []
learning_rate_list = []
for j in range(len(g_) - 1):
line = g_[j].split("\n")
if j == 0:
weights_line = line[0:784]
learning_rate_list.append(
float(line[784].replace("*", "").replace("\n", "")))
else:
weights_line = line[1:785]
learning_rate_list.append(
float(line[785].replace("*", "").replace("\n", "")))
valid_weights_line = []
for l in weights_line:
w_list = l.split("\t")
w_list = w_list[0:len(w_list) - 1]
w_list = [float(i) for i in w_list]
valid_weights_line.append(w_list)
parm_local.append(valid_weights_line)
f.close()
f = open(os.path.join(os.path.dirname(__file__),
"bias_" + str(agent_no) + ".txt"))
content = f.read()
g_ = content.split(
"***\n--------------------------------------------------")
bias_local = []
for j in range(len(g_) - 1):
line = g_[j].split("\n")
if j == 0:
weights_line = line[0]
else:
weights_line = line[1]
b_list = weights_line.split("\t")
b_list = b_list[0:len(b_list) - 1]
b_list = [float(i) for i in b_list]
bias_local.append(b_list)
f.close()
ret = {
'weights': np.asarray(parm_local),
'bias': np.asarray(bias_local),
'learning_rate': np.asarray(learning_rate_list)
}
return ret
def train_with_gradient_and_valuation(agent_list, grad, bi, lr, distr_type, iter_n, g_m):
model_g = {
'weights': g_m[0],
'bias': g_m[1]
}
for i in range(iter_n-1, iter_n):
# i->迭代轮数
gradient_w = np.zeros([784, 10], dtype=np.float32)
gradient_b = np.zeros([10], dtype=np.float32)
for j in agent_list:
gradient_w = np.add(np.multiply(
grad[j][i], 1 / len(agent_list)), gradient_w)
gradient_b = np.add(np.multiply(
bi[j][i], 1 / len(agent_list)), gradient_b)
model_g['weights'] = np.subtract(
model_g['weights'], np.multiply(lr[0][i], gradient_w))
model_g['bias'] = np.subtract(
model_g['bias'], np.multiply(lr[0][i], gradient_b))
test_images = readTestImagesFromFile(False)
test_labels_onehot = readTestLabelsFromFile(False)
m = np.dot(test_images, np.asarray(model_g['weights']))
test_result = m + np.asarray(model_g['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(
tf.argmax(y, 1), tf.arg_max(test_labels_onehot, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy.numpy()
def remove_list_indexed(removed_ele, original_l, ll):
new_original_l = []
for i in original_l:
new_original_l.append(i)
for i in new_original_l:
if i == removed_ele:
new_original_l.remove(i)
for i in range(len(ll)):
if set(ll[i]) == set(new_original_l):
return i
return -1
def shapley_list_indexed(original_l, ll):
for i in range(len(ll)):
if set(ll[i]) == set(original_l):
return i
return -1
def PowerSetsBinary(items):
N = len(items)
set_all = []
for i in range(2 ** N):
combo = []
for j in range(N):
if (i >> j) % 2 == 1:
combo.append(items[j])
set_all.append(combo)
return set_all
def loadHistoryModels(round_num):
f = open(os.path.join(os.path.dirname(__file__),
"gradientplus_models" + str(round_num) + ".txt"), "r")
lines = f.readlines()
ret_models = []
f_ini_p = open(os.path.join(os.path.dirname(__file__),
"initial_model_parameters" + str(round_num-1) + ".txt"), "r")
para_lines = f_ini_p.readlines()
w_paras = para_lines[0].split("\t")
w_paras = [float(i) for i in w_paras]
b_paras = para_lines[1].split("\t")
b_paras = [float(i) for i in b_paras]
w_initial = np.asarray(w_paras, dtype=np.float32).reshape([784, 10])
b_initial = np.asarray(b_paras, dtype=np.float32).reshape([10])
f_ini_p.close()
ret_models.append([w_initial, b_initial])
tem_model = []
for i, line in enumerate(lines):
if i % 2 == 0:
lis = line.strip().replace("[", "").replace("]", "").split(",")
lis = [float(i.strip()) for i in lis]
lis = np.array(lis).reshape([784, 10])
tem_model = [lis]
else:
lis = line.strip().replace("[", "").replace("]", "").split(",")
lis = [float(i.strip()) for i in lis]
lis = np.array(lis)
tem_model.append(lis)
ret_models.append(tem_model)
f.close()
return ret_models
if __name__ == "__main__":
start_time = time.time()
# data_num = np.asarray([5923, 6742, 5958, 6131, 5842])
# agents_weights = np.divide(data_num, data_num.sum())
for index in range(NUM_AGENT):
f = open(os.path.join(os.path.dirname(__file__),
"weights_" + str(index) + ".txt"), "w")
f.close()
f = open(os.path.join(os.path.dirname(__file__),
"bias_" + str(index) + ".txt"), "w")
f.close()
# f = open(os.path.join(os.path.dirname(__file__),
# "gradientplus_models.txt"), "w") #alice
# f.close()
f = open(os.path.join(os.path.dirname(__file__),
"alice" + ".txt"), "w")
f.close()
mnist_train, mnist_test = tf.keras.datasets.mnist.load_data()
DISTRIBUTION_TYPE = "SAME"
federated_train_data_divide = None
federated_train_data = None
if DISTRIBUTION_TYPE == "SAME":
federated_train_data_divide = [get_data_for_federated_agents(
mnist_train, d) for d in range(NUM_AGENT)]
federated_train_data = federated_train_data_divide
f_ini_p = open(os.path.join(os.path.dirname(__file__),
"initial_model_parameters-1.txt"), "r")
para_lines = f_ini_p.readlines()
w_paras = para_lines[0].split("\t")
w_paras = [float(i) for i in w_paras]
b_paras = para_lines[1].split("\t")
b_paras = [float(i) for i in b_paras]
w_initial = np.asarray(w_paras, dtype=np.float32).reshape([784, 10])
b_initial = np.asarray(b_paras, dtype=np.float32).reshape([10])
f_ini_p.close()
initial_model = collections.OrderedDict(
weights=w_initial,
bias=b_initial)
model = initial_model
learning_rate = 0.1
for round_num in range(5):
f = open(os.path.join(os.path.dirname(__file__),
"gradientplus_models" + str(round_num) + ".txt"), "w") # alice
f.close()
local_models = federated_train(
model, learning_rate, federated_train_data)
print("learning rate: ", learning_rate)
# print(local_models[0][0])#第0个agent的weights矩阵
# print(local_models[0][1])#第0个agent的bias矩阵
for local_index in range(len(local_models)):
f = open(os.path.join(os.path.dirname(__file__), "weights_" +
str(local_index) + ".txt"), "a", encoding="utf-8")
for i in local_models[local_index][0]:
line = ""
arr = list(i)
for j in arr:
line += (str(j) + "\t")
print(line, file=f)
print("***" + str(learning_rate) + "***", file=f)
print("-" * 50, file=f)
f.close()
f = open(os.path.join(os.path.dirname(__file__), "bias_" +
str(local_index) + ".txt"), "a", encoding="utf-8")
line = ""
for i in local_models[local_index][1]:
line += (str(i) + "\t")
print(line, file=f)
print("***" + str(learning_rate) + "***", file=f)
print("-" * 50, file=f)
f.close()
m_w = np.zeros([784, 10], dtype=np.float32)
m_b = np.zeros([10], dtype=np.float32)
for local_model_index in range(len(local_models)):
m_w = np.add(np.multiply(
local_models[local_model_index][0], 1 / NUM_AGENT), m_w)
m_b = np.add(np.multiply(
local_models[local_model_index][1], 1 / NUM_AGENT), m_b)
model = {
'weights': m_w,
'bias': m_b
}
f_g = open(os.path.join(os.path.dirname(
__file__), "gradientplus_models" + str(round_num) + ".txt"), "a")
g_w = list(model['weights'].reshape(-1))
g_b = list(model['bias'].reshape(-1))
print(g_w, file=f_g)
print(g_b, file=f_g)
f_g.close()
f = open(os.path.join(os.path.dirname(__file__),
"initial_model_parameters" + str(round_num-1) + ".txt"), "w")
f.close()
f = open(os.path.join(os.path.dirname(__file__),
"initial_model_parameters" + str(round_num-1) + ".txt"), "a")
s = ""
for i in list(model['weights']):
for j in i:
s += str(j)+"\t"
s = s[0:-1]
print(s, file=f)
s = ""
for i in list(model['bias']):
s += str(i)+"\t"
s = s[0:-1]
print(s, file=f)
f.close()
learning_rate = learning_rate * 0.9
loss = federated_eval(model, federated_train_data)
print('round {}, loss={}'.format(round_num, loss))
print(time.time() - start_time)
shapstart_time = time.time() #change cal time
gradient_weights = []
gradient_biases = []
gradient_lrs = []
for ij in range(NUM_AGENT):
model_ = getParmsAndLearningRate(ij)
gradient_weights_local = []
gradient_biases_local = []
learning_rate_local = []
for i in range(len(model_['learning_rate'])):
if i == 0:
gradient_weight = np.divide(np.subtract(initial_model['weights'], model_['weights'][i]),
model_['learning_rate'][i])
gradient_bias = np.divide(np.subtract(initial_model['bias'], model_['bias'][i]),
model_['learning_rate'][i])
else:
gradient_weight = np.divide(np.subtract(model_['weights'][i - 1], model_['weights'][i]),
model_['learning_rate'][i])
gradient_bias = np.divide(np.subtract(model_['bias'][i - 1], model_['bias'][i]),
model_['learning_rate'][i])
gradient_weights_local.append(gradient_weight)
gradient_biases_local.append(gradient_bias)
learning_rate_local.append(model_['learning_rate'][i])
gradient_weights.append(gradient_weights_local)
gradient_biases.append(gradient_biases_local)
gradient_lrs.append(learning_rate_local)
all_sets = PowerSetsBinary([i for i in range(NUM_AGENT)])
models_hository = loadHistoryModels(round_num)
agent_shapley_history = []
for iter_num in range(1, len(gradient_weights[0]) + 1):
group_shapley_value = []
for s in all_sets:
group_shapley_value.append(
train_with_gradient_and_valuation(s, gradient_weights, gradient_biases, gradient_lrs, DISTRIBUTION_TYPE,
iter_num, models_hository[iter_num-1]))
print(str(s) + "\t" +
str(group_shapley_value[len(group_shapley_value) - 1]))
agent_shapley = []
for index in range(NUM_AGENT):
shapley = 0.0
for j in all_sets:
if index in j:
remove_list_index = remove_list_indexed(
index, j, all_sets)
if remove_list_index != -1:
shapley += (group_shapley_value[shapley_list_indexed(j, all_sets)] - group_shapley_value[
remove_list_index]) / (comb(NUM_AGENT - 1, len(all_sets[remove_list_index])))
agent_shapley.append(shapley)
f = open(os.path.join(os.path.dirname(__file__),
"alice" + ".txt"), "a")
print('round {}, loss={}'.format(round_num, agent_shapley), file=f)
agent_shapley_history.append(agent_shapley)
print('round {}, loss={}'.format(
round_num, agent_shapley_history), file=f)
f.close()
print("end_time shap values", time.time() - shapstart_time)
print("end_time", time.time() - start_time)
It seems the source of this code is one of the low-level TFF tutorials, which are really more about introducing users to the core communication abstractions of TFF, as opposed to serving as an introductory guide to federated learning with TFF.
I might suggest a different tutorial, like the one that focuses on image classification for example. This uses TFF's tff.learning.from_keras_model utility to ingest a Keras model directly; if you go this route, you can add layers as you would for any Keras model.
Im trying to setup an Interface for calling the current version of the scipts in a certain Folder.
Im appending my personal script folder and try to import it as its told.
Whenever I press the Button, the following Error Message appears:
Error: NameError: file line 1: name 'setVars' is not defined
I´ve tried numerous possible import techniques. But the Errors keep popping up.
I´ll attach the Interface Script and one of the scipts that are ment to be called.
Interface:
import maya.cmds as mc
import sys
import os
import importlib
FolderContent = []
SysPath = 'C:/Users/[Username]/Documents/maya/2019/scripts/Personal'
sys.path.append(SysPath)
print sys.path
for file in os.listdir(SysPath):
if file.endswith(".py"):
FolderContent.append(file)
def UI(FolderContent):
x = 0
if mc.window("Run_Script", ex = True):
mc.deleteUI("Run_Script")
popUp = mc.window("Run_Script")
mc.gridLayout(nc = 1, ch = 30, cw = 300)
for i in FolderContent:
ScriptName = FolderContent[x]
mc.button(label = FolderContent[x], command = 'callFunc(\'%s\')' % (ScriptName), bgc = (0.25, 0.25, 0.25))
x += 1
mc.button(label = 'Cancel', command = 'close()', bgc = (0.92, 0.4, 0.4))
mc.window(popUp, e = True, s = False, h = 50, w = 100)
mc.showWindow()
UI(FolderContent)
def callFunc(ScriptName):
ScriptName = ScriptName[0:-3]
mymodule = importlib.import_module(ScriptName)
def close():
mc.deleteUI("Run_Script")
Example Script to be called:
import maya.cmds as mc
mc.select(hi = True)
BaseChain = mc.ls(sl = True)
ChainLength = len(BaseChain) - 1
###################################################################################
if mc.window("SetVariables", ex = True):
mc.deleteUI("SetVariables")
popUp = mc.window("SetVariables")
mc.gridLayout(nc = 2, ch = 30, cw = 200)
mc.text(label = "Side")
mc.textField("side")
mc.text(label = "Limb")
mc.textField("limb")
mc.text(label = "FK-Amount")
mc.textField("amount", fi = ChainLength)
cmd = 'setVars()'
close = 'closeWindow()'
mc.button(label = "OK", command = cmd, bgc = (0.2, 0.92, 0.75))
mc.button(label = "Cancel", command = close, bgc = (0.92, 0.4, 0.4))
mc.window(popUp, e = True, s = False, h = 50, w = 100)
mc.showWindow()
###################################################################################
def createChains(side, limb, amount):
mc.select(hi = True)
BndChain = mc.ls(sl = True)
x = 0
for i in BndChain:
mc.rename(BndChain[x], side + '_' + limb + '_BND_Chain_0' + str(x + 1))
x += 1
BndChain = mc.ls(sl = True)
ChainLength = len(BndChain)
mc.duplicate( n = side + '_' + limb + '_FK_Chain_01')
FkChain = mc.ls(sl = True)
mc.duplicate( n = side + '_' + limb + '_IK_Chain_01')
IkChain = mc.ls(sl = True)
parentChains(ChainLength, FkChain, IkChain, BndChain, side, limb, amount)
###################################################################################
def parentChains(ChainLength, FkChain, IkChain, BndChain, side, limb, amount):
x = 0
while x < ChainLength:
mc.select(FkChain[x])
mc.select(IkChain[x], add = True)
mc.select(BndChain[x], add = True)
mc.parentConstraint(mo = False)
x += 1
FKChainCtrl(ChainLength, FkChain, IkChain, BndChain, side, limb, amount)
###################################################################################
def FKChainCtrl(ChainLength, FkChain, IkChain, BndChain, side, limb, amount):
listLength = len(FkChain)
invertedNumber = listLength - int(amount)
doFK = listLength - invertedNumber
x = 0
Fk_ctrls = []
while x < doFK:
queryObj = FkChain[x]
currCtrl = mc.circle(n = side + '_' + limb + '_' + 'FK' + '_' + 'CTRL' + '_01', nr = [1, 0, 0], sw = 180)[0]
mc.curveRGBColor('translate.X', 1, 0, 1)
Fk_ctrls.append(currCtrl)
mc.parentConstraint(queryObj, currCtrl, mo = False)
mc.select(cl = True)
mc.select(queryObj)
mc.select(currCtrl, add = True)
mc.RemoveConstraintTarget()
mc.select(cl = True)
mc.select(currCtrl)
sel = mc.ls(sl = True)[0]
mc.FreezeTransformations(sel)
mc.parentConstraint(currCtrl, queryObj, mo = True)
if(x > 0):
mc.parent(Fk_ctrls[-1], Fk_ctrls[-2])
x += 1
mc.hide(FkChain)
IKChainCtrl(IkChain, BndChain, side, limb)
###################################################################################
def IKChainCtrl(IkChain, BndChain, side, limb):
currHandle = mc.ikHandle(n = side + '_' + limb + '_IkHandle', sj = IkChain[0], ee = IkChain[-1])[0]
mc.hide()
currHandlePos = mc.xform(currHandle, q = True, t = True, ws = True)
currCtrl = mc.circle(n = currHandle + '_CTRL', nr = [1, 0, 0], r = 1.5)
# currCtrl = mc.curve(n = currHandle + '_CTRL', d = 1, p = [(-0.75, 0, 0), (-0.75, 0, 2.5), (-2.5, 0, 2.5), (0, 0, 6.25), (2.5, 0, 2.5), (0.75, 0, 2.5), (0.75, 0, 0), (0.75, 0, -2.5), (2.5, 0, -2.5), (0, 0, -6.25), (-2.5, 0, -2.5), (-0.75, 0, -2.5), (-0.75, 0, 0)])
# mc.setAttr(currCtrl + '.overrideEnabled', 1)
# mc.setAttr(currCtrl + '.overrideColor', 21)
mc.select(IkChain[-2])
mc.select(currCtrl, add = True)
mc.parentConstraint(mo = False)
mc.select(IkChain[-2])
mc.select(currCtrl, add = True)
sel = mc.ls(sl = True)
mc.select(sel[0:-1], r = True)
mc.RemoveConstraintTarget()
mc.xform(currCtrl, t = [currHandlePos[0], currHandlePos[1], currHandlePos[2]])
mc.select(currCtrl, r = True)
mc.FreezeTransformations()
# mc.select('makeNurbCircle3', d = True)
mc.duplicate()
mc.parentConstraint(currCtrl, currHandle, mo = True)
mc.rename(side + '_' + limb + '_IkHandle_CTRL1', side + '_' + limb + '_Switch')
mc.xform(s = [1.5, 1.5, 1.5])
SwitchCtrl = mc.ls(sl = True)
mc.pointConstraint(BndChain[-1], SwitchCtrl)
currCtrl = mc.ls(sl = True)[0]
mc.hide(IkChain)
AddSwitchAttributes(currCtrl, side, limb)
###################################################################################
def AddSwitchAttributes(currCtrl, side, limb):
mc.addAttr(currCtrl, sn = 'FK', nn = 'FK', min = 0, max = 1, dv = 0, k = True, h = True)
mc.addAttr(currCtrl, sn = 'IK', nn = 'IK', min = 0, max = 1, dv = 1, k = True, h = True)
mc.addAttr(currCtrl, sn = 'Switch', nn = 'FK|IK Switch', min = 0, max = 1, dv = 1, k = True)
mc.connectAttr(side + '_' + limb + '_Switch.FK', side + '_' + limb + '_FK_CTRL_01.visibility')
mc.connectAttr(side + '_' + limb + '_Switch.IK', side + '_' + limb + '_IkHandle_CTRL.visibility')
i = 1
while i < ChainLength + 2:
mc.setDrivenKeyframe(side + '_' + limb + '_BND_Chain_0' + str(i) + '_parentConstraint1.' + side + '_' + limb + '_IK_Chain_0' + str(i) + 'W1', cd = side + '_' + limb + '_Switch.Switch', v = 1.0)
mc.setDrivenKeyframe(side + '_' + limb + '_BND_Chain_0' + str(i) + '_parentConstraint1.' + side + '_' + limb + '_FK_Chain_0' + str(i) + 'W0', cd = side + '_' + limb + '_Switch.Switch', v = 0.0)
i += 1
mc.setDrivenKeyframe(side + '_' + limb + '_Switch.IK', cd = side + '_' + limb + '_Switch.Switch', v = 1.0)
mc.setDrivenKeyframe(side + '_' + limb + '_Switch.FK', cd = side + '_' + limb + '_Switch.Switch', v = 0.0)
mc.setAttr(side + '_' + limb + '_Switch.Switch', 0)
y = 1
while y < ChainLength + 2:
mc.setDrivenKeyframe(side + '_' + limb + '_BND_Chain_0' + str(y) + '_parentConstraint1.' + side + '_' + limb + '_IK_Chain_0' + str(y) + 'W1', cd = side + '_' + limb + '_Switch.Switch', v = 0.0)
mc.setDrivenKeyframe(side + '_' + limb + '_BND_Chain_0' + str(y) + '_parentConstraint1.' + side + '_' + limb + '_FK_Chain_0' + str(y) + 'W0', cd = side + '_' + limb + '_Switch.Switch', v = 1.0)
y += 1
mc.setDrivenKeyframe(side + '_' + limb + '_Switch.IK', cd = side + '_' + limb + '_Switch.Switch', v = 0.0)
mc.setDrivenKeyframe(side + '_' + limb + '_Switch.FK', cd = side + '_' + limb + '_Switch.Switch', v = 1.0)
mc.setAttr(side + '_' + limb + '_Switch.Switch', 1)
mc.select(cl = True)
###################################################################################
def setVars():
side = mc.textField("side", q = True, tx = True)
limb = mc.textField("limb", q = True, tx = True)
amount = mc.textField("amount", q = True, tx = True)
createChains(side, limb, amount)
closeWindow()
###################################################################################
def closeWindow():
mc.deleteUI("SetVariables")
Thank you for your help!
Okay, I tried it now and there are some problems. I suppose you executed your main script directly from within maya? Because if I put it into a module and load it, I get a similiar error that "close()" is not found as well as "callFunc()" is not found. Both have the same reason which can easily be solved by not using strings.
In your main script, you can replace the "close()" in the button command with the same procedure as mentioned above:
mc.button(label = 'Cancel', command = close, bgc = (0.92, 0.4, 0.4))
Since the button command automatically adds an argument you will have an default argument to the close() command, something like close(dummyArg).
The callFunc() is a little bit more complicated because you use an argument. This can be solved by partial e.g.:
from functools import partial
for value in FolderContent:
mc.button(label = value, command = partial(callFunc, value), bgc = (0.25, 0.25, 0.25))
The button call will add another boolean to the call, so that the callFunc will receive another argument which can be captured by adding another argument to the callFunc definition like callFunc(scriptName, value).
This way your function object is used not a string. But now you have another problem because if you load the module the first time, you will get an error
NameError: global name 'close' is not defined #
That's because the function is not yet defined at the moment it us used, so you have to put the function definition at before the UI definition function.
Now if your script is imported you will get similiar problems with functions defined by strings, but they can be solved the same way as in the main module.
Without trying... I suppose the reason is that you use the string "setVars()" as command name. If you call it from your main module, there is no setVars(), but a a function called WhatEverYourModuelNameIs.setVars().
You could either try to import all like this:
from WhatEverYourModuelNameIs import *
What is not a really good solution, or you can try to use the function object directly as parameter for the button:
mc.button(label = "OK", command = setVars, bgc = (0.2, 0.92, 0.75))
Sorry for the delay guys, but Im glad to report that is working now!
Turn out the problem was indeed, that i didnt import the scripts before executing the UI.
So thats the dynamic UI at this point:
import maya.cmds as mc
import sys
import os
FolderContent = []
SysPath = 'C:/Users/mkzie/Documents/maya/2019/scripts/Personal'
sys.path.append(SysPath)
for file in os.listdir(SysPath):
if file.endswith(".py"):
FolderContent.append(file[0:-3])
print FolderContent
x = 0
for i in FolderContent:
exec('import %s') % (FolderContent[x])
x += 1
def UI(FolderContent):
x = 0
if mc.window("CXL_Interface", ex = True):
mc.deleteUI("CXL_Interface")
Interface = mc.window("CXL_Interface", s = True, rtf = True, h = 1)
# mc.scrollLayout()
for i in FolderContent:
ScriptName = FolderContent[x]
col = mc.columnLayout(cat = ('left', -1))
mc.frameLayout(l = ScriptName, cll = True, cl = True, w = 300, mh = 5, mw = 0)
exec('%s.init()') % (ScriptName)
mc.setParent( '..' )
mc.setParent( '..' )
x += 1
mc.setParent( '..' )
mc.setParent( '..' )
mc.separator(h = 1, st = 'none')
mc.columnLayout(h = 45, adj = True)
mc.separator(h = 5, st = 'shelf')
mc.button(l = 'Close Interface', c = 'close()', h = 40, w = 300, bgc = (.9, 0.2, 0.3))
mc.window(Interface, e = True, h = 1, w = 1)
mc.showWindow()
UI(FolderContent)
def callFunc(ScriptName):
exec('%s.init()') % (ScriptName)
def close():
mc.deleteUI("CXL_Interface")
By doing that before calling the UI, its even not a problem to use strings in the respective sripts:
mc.button(l = "OK", c = 'BuildKinematics.setVars()', bgc = (.4, .75, .4))
I think its a bit rough yet.
I'm just starting out doing stuff like that( like 4 weeks maybe), so improvements are very much appreciated.
Thank you very much for the big help!
Cheers.
I'm trying to optmize a problem of clients/facility allocation and I'm using pyomo to do so. I have the following problem that appear and cannot solve it even if I initialize all the values of Model.z to 0; the problems is still infeasible
from __future__ import division
import pyomo.environ as pyo
#from pyomo.environ import *
from pyomo.opt import SolverFactory
import sys
import numpy as np
import sys
import os
from time import perf_counter
from pulp import *
from pyomo.util.infeasible import log_infeasible_constraints
#print(sys.path)
opt = pyo.SolverFactory('glpk')
opt.options['threads'] =4
#import instance
model = pyo.ConcreteModel()
# n is the number of facility related to J
# m is the number of clients related to I
# t is the number of periods relatdd to T
# H is the maximum number of clients that can be served
[m,n,t,H] = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, max_rows = 1)
f_values = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, max_rows = 4,skip_header=1)
c_values = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, skip_footer = 1,skip_header=5)
p_values = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, skip_header=405)
model.J = pyo.Set(initialize=[int(i)+1 for i in range(n)])
model.T= pyo.Set(initialize=[int(i)+1 for i in range(t)])
model.T.pprint()
model.I = pyo.Set(initialize=[int(i)+1 for i in range(m)])
model.H = pyo.Set(initialize=[int(i)+1 for i in range(H)])
# Generation of Subsets of all clients
client_list = [int(i)+1 for i in range(100)]
list_H = [int(i)+1 for i in range(H)]
possible_groups_of_clients=[]
for i in list_H:
possible_groups_of_clients += [list(elem) for elem in itertools.combinations(client_list,i)]
possible_groups_of_clients = possible_groups_of_clients[0:5] #Only select the first 5 subsets
model.K = pyo.Set(initialize=[int(i)+1 for i in range(len(possible_groups_of_clients))])
p_val = np.zeros((len(model.K),len(model.J),len(model.T)))
for t in model.T:
t=t-1 #Pyomo starts at 1
for j in model.J:
j=j-1
count = 0
for k in possible_groups_of_clients:
#k=k+1
c_sum=0;
for p in k:
if t==0:
c_sum = c_sum +c_values[p][j]
elif t==1:
c_sum = c_sum +c_values[p+100][j]
elif t==2:
c_sum = c_sum +c_values[p+200][j]
elif t==3:
c_sum = c_sum +c_values[p+300][j]
#print(f_values[t][j])
p_val[count][j][t] = f_values[t][j]+ c_sum
count=count+1
model.p_val = pyo.Param(model.K,model.J,model.T, initialize = lambda model,k,j,t: p_val[k-1][j-1][t-1])
#Matrice Aik
A = [[0 for k in range(len(possible_groups_of_clients))] for i in range(100)]
#model.p_val = pyo.Param(model.T,model.J,model.K, initialize = lambda model,t,j,k: A[t-1][j-1][k-1])
model.A = pyo.Param(model.I, model.K, mutable= True,initialize= lambda model,i,k: A[i-1][k-1])
count=1
for i in possible_groups_of_clients:
for k in range(100):
k=k+1
#print(k)
if k in i:
model.A[k,count]=1
else:
model.A[k,count]=0
count=count+1
model.z = pyo.Var(model.K,model.J,model.T,initialize= 0,within=pyo.NonNegativeReals,bounds=(0,1))
a=range(len(possible_groups_of_clients))
print(p_values)
# Minization of objective functions
# Constraint unique client
model.uniq_client = pyo.Constraint(
model.I,
rule=lambda model, i: sum(model.A[i,k]*model.z[k,j,t] for t in model.T for j in model.J for k in model.K) == 1
)
model.unique_service_groupclients = pyo.Constraint(
model.J, model.T,
rule=lambda model,j,t: sum(model.z[k,j,t] for k in model.K)<=1
)
model.min_factory_perperiod = pyo.Constraint(
model.T,
rule=lambda model, t: sum(model.z[k,j,t] for j in model.J for k in model.K )>=p_values[t-1]
)
model.obj = pyo.Objective(rule= lambda model:sum(model.p_val[k,j,t]*model.z[k,j,t] for t in model.T for j in model.J for k in model.K ), sense= pyo.minimize)
model.pprint()
Constraint Group Client
sol = pyo.SolverFactory('glpk',).solve(model).write()
log_infeasible_constraints(model)
# ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# ----------------------------------------------------------
Problem:
- Name: unknown
Lower bound: -inf
Upper bound: inf
Number of objectives: 1
Number of constraints: 145
Number of variables: 201
Number of nonzeros: 601
Sense: minimize
# ----------------------------------------------------------
# Solver Information
# ----------------------------------------------------------
Solver:
- Status: ok
Termination condition: infeasible
Statistics:
Branch and bound:
Number of bounded subproblems: 0
Number of created subproblems: 0
Error rc: 0
Time: 0.008042335510253906
ERROR: evaluating object as numeric value: z[1,1,1]
(object: <class 'pyomo.core.base.var._GeneralVarData'>)
No value for uninitialized NumericValue object z[1,1,1]
ERROR: evaluating object as numeric value: 1.0 - (A[1,1]*z[1,1,1] +
A[1,2]*z[2,1,1] + A[1,3]*z[3,1,1] + A[1,4]*z[4,1,1] + A[1,5]*z[5,1,1] +
A[1,1]*z[1,2,1] + A[1,2]*z[2,2,1] + A[1,3]*z[3,2,1] + A[1,4]*z[4,2,1] +
A[1,5]*z[5,2,1] + A[1,1]*z[1,3,1] + A[1,2]*z[2,3,1] + A[1,3]*z[3,3,1] +
A[1,4]*z[4,3,1] + A[1,5]*z[5,3,1] + A[1,1]*z[1,4,1] + A[1,2]*z[2,4,1] +
A[1,3]*z[3,4,1] + A[1,4]*z[4,4,1] + A[1,5]*z[5,4,1] + A[1,1]*z[1,5,1] +
A[1,2]*z[2,5,1] + A[1,3]*z[3,5,1] + A[1,4]*z[4,5,1] + A[1,5]*z[5,5,1] +
A[1,1]*z[1,6,1] + A[1,2]*z[2,6,1] + A[1,3]*z[3,6,1] + A[1,4]*z[4,6,1] +
A[1,5]*z[5,6,1] + A[1,1]*z[1,7,1] + A[1,2]*z[2,7,1] + A[1,3]*z[3,7,1] +
A[1,4]*z[4,7,1] + A[1,5]*z[5,7,1] + A[1,1]*z[1,8,1] + A[1,2]*z[2,8,1] +
A[1,3]*z[3,8,1] + A[1,4]*z[4,8,1] + A[1,5]*z[5,8,1] + A[1,1]*z[1,9,1] +
A[1,2]*z[2,9,1] + A[1,3]*z[3,9,1] + A[1,4]*z[4,9,1] + A[1,5]*z[5,9,1] +
A[1,1]*z[1,10,1] + A[1,2]*z[2,10,1] + A[1,3]*z[3,10,1] + A[1,4]*z[4,10,1]
+ A[1,5]*z[5,10,1] + A[1,1]*z[1,1,2] + A[1,2]*z[2,1,2] + A[1,3]*z[3,1,2] +
A[1,4]*z[4,1,2] + A[1,5]*z[5,1,2] + A[1,1]*z[1,2,2] + A[1,2]*z[2,2,2] +
A[1,3]*z[3,2,2] + A[1,4]*z[4,2,2] + A[1,5]*z[5,2,2] + A[1,1]*z[1,3,2] +
A[1,2]*z[2,3,2] + A[1,3]*z[3,3,2] + A[1,4]*z[4,3,2] + A[1,5]*z[5,3,2] +
A[1,1]*z[1,4,2] + A[1,2]*z[2,4,2] + A[1,3]*z[3,4,2] + A[1,4]*z[4,4,2] +
A[1,5]*z[5,4,2] + A[1,1]*z[1,5,2] + A[1,2]*z[2,5,2] + A[1,3]*z[3,5,2] +
A[1,4]*z[4,5,2] + A[1,5]*z[5,5,2] + A[1,1]*z[1,6,2] + A[1,2]*z[2,6,2] +
A[1,3]*z[3,6,2] + A[1,4]*z[4,6,2] + A[1,5]*z[5,6,2] + A[1,1]*z[1,7,2] +
A[1,2]*z[2,7,2] + A[1,3]*z[3,7,2] + A[1,4]*z[4,7,2] + A[1,5]*z[5,7,2] +
A[1,1]*z[1,8,2] + A[1,2]*z[2,8,2] + A[1,3]*z[3,8,2] + A[1,4]*z[4,8,2] +
A[1,5]*z[5,8,2] + A[1,1]*z[1,9,2] + A[1,2]*z[2,9,2] + A[1,3]*z[3,9,2] +
A[1,4]*z[4,9,2] + A[1,5]*z[5,9,2] + A[1,1]*z[1,10,2] + A[1,2]*z[2,10,2] +
A[1,3]*z[3,10,2] + A[1,4]*z[4,10,2] + A[1,5]*z[5,10,2] + A[1,1]*z[1,1,3] +
A[1,2]*z[2,1,3] + A[1,3]*z[3,1,3] + A[1,4]*z[4,1,3] + A[1,5]*z[5,1,3] +
A[1,1]*z[1,2,3] + A[1,2]*z[2,2,3] + A[1,3]*z[3,2,3] + A[1,4]*z[4,2,3] +
A[1,5]*z[5,2,3] + A[1,1]*z[1,3,3] + A[1,2]*z[2,3,3] + A[1,3]*z[3,3,3] +
A[1,4]*z[4,3,3] + A[1,5]*z[5,3,3] + A[1,1]*z[1,4,3] + A[1,2]*z[2,4,3] +
A[1,3]*z[3,4,3] + A[1,4]*z[4,4,3] + A[1,5]*z[5,4,3] + A[1,1]*z[1,5,3] +
A[1,2]*z[2,5,3] + A[1,3]*z[3,5,3] + A[1,4]*z[4,5,3] + A[1,5]*z[5,5,3] +
A[1,1]*z[1,6,3] + A[1,2]*z[2,6,3] + A[1,3]*z[3,6,3] + A[1,4]*z[4,6,3] +
A[1,5]*z[5,6,3] + A[1,1]*z[1,7,3] + A[1,2]*z[2,7,3] + A[1,3]*z[3,7,3] +
A[1,4]*z[4,7,3] + A[1,5]*z[5,7,3] + A[1,1]*z[1,8,3] + A[1,2]*z[2,8,3] +
A[1,3]*z[3,8,3] + A[1,4]*z[4,8,3] + A[1,5]*z[5,8,3] + A[1,1]*z[1,9,3] +
A[1,2]*z[2,9,3] + A[1,3]*z[3,9,3] + A[1,4]*z[4,9,3] + A[1,5]*z[5,9,3] +
A[1,1]*z[1,10,3] + A[1,2]*z[2,10,3] + A[1,3]*z[3,10,3] + A[1,4]*z[4,10,3]
+ A[1,5]*z[5,10,3] + A[1,1]*z[1,1,4] + A[1,2]*z[2,1,4] + A[1,3]*z[3,1,4] +
A[1,4]*z[4,1,4] + A[1,5]*z[5,1,4] + A[1,1]*z[1,2,4] + A[1,2]*z[2,2,4] +
A[1,3]*z[3,2,4] + A[1,4]*z[4,2,4] + A[1,5]*z[5,2,4] + A[1,1]*z[1,3,4] +
A[1,2]*z[2,3,4] + A[1,3]*z[3,3,4] + A[1,4]*z[4,3,4] + A[1,5]*z[5,3,4] +
A[1,1]*z[1,4,4] + A[1,2]*z[2,4,4] + A[1,3]*z[3,4,4] + A[1,4]*z[4,4,4] +
A[1,5]*z[5,4,4] + A[1,1]*z[1,5,4] + A[1,2]*z[2,5,4] + A[1,3]*z[3,5,4] +
A[1,4]*z[4,5,4] + A[1,5]*z[5,5,4] + A[1,1]*z[1,6,4] + A[1,2]*z[2,6,4] +
A[1,3]*z[3,6,4] + A[1,4]*z[4,6,4] + A[1,5]*z[5,6,4] + A[1,1]*z[1,7,4] +
A[1,2]*z[2,7,4] + A[1,3]*z[3,7,4] + A[1,4]*z[4,7,4] + A[1,5]*z[5,7,4] +
A[1,1]*z[1,8,4] + A[1,2]*z[2,8,4] + A[1,3]*z[3,8,4] + A[1,4]*z[4,8,4] +
A[1,5]*z[5,8,4] + A[1,1]*z[1,9,4] + A[1,2]*z[2,9,4] + A[1,3]*z[3,9,4] +
A[1,4]*z[4,9,4] + A[1,5]*z[5,9,4] + A[1,1]*z[1,10,4] + A[1,2]*z[2,10,4] +
A[1,3]*z[3,10,4] + A[1,4]*z[4,10,4] + A[1,5]*z[5,10,4])
(object: <class 'pyomo.core.expr.numeric_expr.SumExpression'>)
No value for uninitialized NumericValue object z[1,1,1]
Traceback (most recent call last):
File "main.py", line 246, in <module>
log_infeasible_constraints(model)
File "/home/john/.local/lib/python3.6/site-packages/pyomo /util/infeasible.py", line 25, in log_infeasible_constraints
if constr.equality and fabs(value(constr.lower - constr.body)) >= tol:
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/numvalue.py", line 226, in value
tmp = obj(exception=True)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/numeric_expr.py", line 222, in __call__
return evaluate_expression(self, exception)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/visitor.py", line 973, in evaluate_expression
return visitor.dfs_postorder_stack(exp)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/visitor.py", line 518, in dfs_postorder_stack
flag, value = self.visiting_potential_leaf(_sub)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/visitor.py", line 893, in visiting_potential_leaf
return True, value(node)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/numvalue.py", line 230, in value
% (obj.name,))
ValueError: No value for uninitialized NumericValue object z[1,1,1]
I got this error whatever I try and I don't understand why Z should be initialized as it is the variable I'm trying to minimize
I'm newer to Python and I've been trying to build a Naive Bayes classifier, but it seems to be prioritizing Spam over Ham. I know it's a lot to ask, but I was hoping someone who was familiar with Naive Bayes could point out what I've done wrong. As a side note: I skipped the denominator portion of the Naive Bayes equation; a common denominator shouldn't make a difference, right?
Here's a link to the guide I followed: https://towardsdatascience.com/unfolding-na%C3%AFve-bayes-from-scratch-2e86dcae4b01
And here's my code:
import csv
ham = 0
spam = 0
dictionarySpam = {}
dictionaryHam = {}
dictionaryTotal = {}
userString = input("Enter your string")
userString = userString.replace('.', ' ')
a = "!##$%^&*()_+=-?,><':;[]/"
userStringList = userString.split()
print(userStringList)
totalNumHam = 0
with open('spam.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
if "ham" in row[0]:
ham += 1
elif "spam" in row[0]:
spam += 1
words = row[1].split()
for word in words:
word = word.replace('.', ' ')
for char in a:
word = word.replace(char, '')
dictionaryTotal[word.lower()] = dictionaryTotal.get(word.lower(), 0) + 1
if "ham" in row[0].lower():
dictionaryHam[word.lower()] = dictionaryHam.get(word.lower(), 0) + 1
elif "spam" in row[0].lower():
dictionarySpam[word.lower()] = dictionarySpam.get(word.lower(), 0) + 1
probHam = 1;
probSpam = 1;
print("HAM cases: ", ham)
print("SPAM cases: ", spam)
print(dictionaryHam)
print(dictionarySpam)
for item in userStringList:
if item in dictionaryHam:
probHam = probHam * ((dictionaryHam[item] + 1) / (sum(dictionaryHam.values()) + len(dictionaryTotal) + 1))
elif item not in dictionaryHam:
probHam = probHam * (1 / (sum(dictionaryHam.values()) + len(dictionaryTotal) + 1))
if item in dictionarySpam:
probSpam = probSpam * ((dictionarySpam[item] + 1) / (sum(dictionarySpam.values()) + len(dictionaryTotal) + 1))
elif item not in dictionaryHam:
probHam = probSpam * (1 / (sum(dictionarySpam.values()) + len(dictionaryTotal) + 1))
print("OUT: ", probHam)
probHam = probHam * (ham / (ham + spam))
probSpam = probSpam * (spam / (ham + spam))
print(probHam)
print(probSpam)
if probHam > probSpam:
print("This message is HAM")
else:
print("This message is SPAM")
I think you have messed up with your brackes in line:
probHam = probHam * ((dictionaryHam[item] + 1) / (sum(dictionaryHam.values()) + len(dictionaryTotal) + 1))
Environment
I am using Python 3 and my OS is Windows 7.
I understand that some commands have changed from the transition from python 2.7 to 3 (What I have used).
Problem
The variable is temporary but is this:
(((((0, 7), 7), 8), 4), 5)
Here is the code to get rid of the brackets:
randy = randy.replace(")", "")
randy = randy.replace("(", "")
randy = randy.replace(" ", "")
When it tries to execute the replace function, I get thrown an error:
Traceback (most recent call last):
File "<string>", line 248, in run_nodebug
File "Criptic.py", line 134, in <module>
randy = randy.replace(")", "")
AttributeError: 'tuple' object has no attribute 'replace'
Edit:
Here is all of the code:
#Import string
import string
import random
#Input user data
text = input("Enter your text to be converted: ")
#Test Print
print("-------------------------")
print("Your text is: ",text)
#Break up all the data
data = list(text)
#Lowercase
count = text.count("a")
count1 = text.count("b")
count2 = text.count("c")
count3 = text.count("d")
count4 = text.count("e")
count5 = text.count("f")
count6 = text.count("g")
count7 = text.count("h")
count8 = text.count("i")
count9 = text.count("j")
count10 = text.count("k")
count11 = text.count("l")
count12 = text.count("m")
count13 = text.count("n")
count14 = text.count("o")
count15 = text.count("p")
count16 = text.count("q")
count17 = text.count("r")
count18 = text.count("s")
count19 = text.count("t")
count20 = text.count("u")
count21 = text.count("v")
count22 = text.count("w")
count23 = text.count("x")
count24 = text.count("y")
count25 = text.count("z")
count26 = text.count("A")
#Uppercase
count27 = text.count("B")
count28 = text.count("C")
count29 = text.count("D")
count30 = text.count("E")
count31 = text.count("F")
count32 = text.count("G")
count33 = text.count("H")
count34 = text.count("I")
count35 = text.count("J")
count36 = text.count("K")
count37 = text.count("L")
count38 = text.count("M")
count39 = text.count("N")
count40 = text.count("O")
count41 = text.count("P")
count42 = text.count("Q")
count43 = text.count("R")
count44 = text.count("S")
count45 = text.count("T")
count46 = text.count("U")
count47 = text.count("V")
count48 = text.count("W")
count49 = text.count("X")
count50 = text.count("Y")
count51 = text.count("Z")
#Other Characters
count52 = text.count(" ")
count53 = text.count("?")
count54 = text.count("#")
count55 = text.count("(")
count56 = text.count(")")
count57 = text.count(".")
#Numbers
count58 = text.count("1")
count59 = text.count("2")
count60 = text.count("3")
count61 = text.count("4")
count62 = text.count("5")
count63 = text.count("6")
count64 = text.count("7")
count65 = text.count("8")
count66 = text.count("9")
count67 = text.count("0")
#Counting how many characters in the sentence
finalcount = count + count1 + count2 + count3 + count4 + count5 + count6 + count7 + count8 + count9 + count10 + count11 + count12 + count13 + count14 + count15 + count16 + count17 + count18 + count19 + count20 + count21 + count22 + count23 + count24 + count25 + count26 + count27 + count28 + count29 + count31 + count32 + count33 + count34 + count35 + count36 + count37 + count38 + count39 + count40 + count41 + count42 + count43 + count44 + count45 + count46 + count47 + count48 + count49 + count50 + count51 + count52 + count53 + count54 + count55 + count56 + count57 + count58 + count59 + count60 + count61 + count62 + count63 + count64 + count65 + count66 + count67
#Final count of Characters
print(" Chars: ",finalcount)
print("-------------------------")
char = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!##$%^&*()?_+-=1234567890 "
charnum = 74 + 11
list(char)
randy = 0
num = 0
while num < finalcount:
rand = random.randrange(1,9)
randy = randy,rand
finalcount = finalcount - 1
if rand == 1:
print(text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 2:
print(char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 3:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 4:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 5:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 6:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 7:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],",")
if rand == 8:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],char[random.randrange(0,charnum)],",")
if rand == 9:
print(char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],char[random.randrange(0,charnum)],text[finalcount],",")
randy = randy.replace(")", "")
randy = randy.replace("(", "")
randy = randy.replace(" ", "")
print("-------------------------")
print("::::::::Completed::::::::")
print("-------------------------")
print(randy)
print("-This is your code unlock key. Without this code it will not decript!!!! ")
print("-------------------------")
You have a tuple (a sort of list) of numbers, and you want to make that a string. You can't replace the brackets, they aren't a part of the variable, they are just a part of its representation.
Instead you should use join() and str()
result = " ".join(str(x) for x in randy)
However, this probably will not give the right result either, as it is a nested list of tuples. You probably mean: randy = randy + (rand,) instead if randy = randy,rand.
randy = randy,rand
This notation creates a tuple of two elements, tuples do not have a method called replace.
If you just want to concatenate the numbers into a list representation separated by commas, you could do this in your loop:
randy = []
num = 0
while num < finalcount:
rand = random.randrange(1,9)
randy.append(rand)
Then you remove the replace lines, and instead do this
randy = ",".join(randy)
to get a string that has the values separated by commas.
You should maybe add the output that you would like to get to your question, because at the moment it's not quite clear what you want to do exactly.
exam_st_date = (11, 12, 2014)
# Sample Output : The examination will start from : 11 / 12 / 2014
newtup = str(exam_st_date).replace(',', ' /')
print(newtup)
look at mine example should give you some results
To delete a list of characters, you could use the string's translate method:
import string
name = "(((((0, 7), 7), 8), 4), 5)"
table = string.maketrans( '', '', )
print name.translate(table,"()")