What I want to do is ONLY print a table's contents within a table. For example:
local stats = {
table1 = {
tTable1 =
{
data = 1
},
tTable2 =
{
data2 = 2
},
tTable3 =
{
data3 = 3
},
}
}
I don't really care about table1 or all the tTables but rather the information in the data variables. How can I print them?
This is a snippet of my real code:
local stats = {
[1] = {
[1] = {
[1] = 1,
[2] = -1,
[3] = -1,
["n"] = 3,
},
[2] = {
[1] = nuclearcraft:cooler,
[2] = 10,
["n"] = 2,
},
["n"] = 2,
},
[2] = {
[1] = {
[1] = 2,
[2] = -1,
[3] = -1,
["n"] = 3,
},
[2] = {
[1] = nuclearcraft:cell_block,
[2] = 0,
["n"] = 2,
},
["n"] = 2,
},
[3] = {
[1] = {
[1] = 3,
[2] = -1,
[3] = -1,
["n"] = 3,
},
[2] = {
[1] = nuclearcraft:cooler,
[2] = 10,
["n"] = 2,
},
["n"] = 2,
},
}
This code actually goes on for a bit longer than this. In the real code, I don't care for any of the data except for the areas where it says "nuclearcraft" and the number beneath it.
recursive table traversal is suitable for this case:
local function TablePrint(t)
for k,v in pairs(t) do
if type(v)=="table" then
print(k)
TablePrint(v)
else
print('\t',k,v)
end
end
end
TablePrint(stats)
result:
table1
tTable3
data3 3
tTable2
data2 2
tTable1
data 1
keep in mind that the order of non-index values in the table is not defined
Related
I've been trying figure out what I have done wrong for many hours, but just can't figure out. I've even looked at other basic Neural Network libraries to make sure that my gradient descent algorithms were correct, but it still isn't working.
I'm trying to teach it XOR but it outputs -
input (0 0) | 0.011441891321516094
input (1 0) | 0.6558508610135193
input (0 1) | 0.6558003273099053
input (1 1) | 0.6563021185296245
after 1000 trainings, so clearly there's something wrong.
The code is written in lua and I created the Neural Network from raw data so you can easily understand how the data is formatted.
- Training code -
math.randomseed(os.time())
local nn = require("NeuralNetwork")
local network = nn.newFromRawData({
["activationFunction"] = "sigmoid",
["learningRate"] = 0.3,
["net"] = {
[1] = {
[1] = {
["value"] = 0
},
[2] = {
["value"] = 0
}
},
[2] = {
[1] = {
["bias"] = 1,
["netInput"] = 0,
["value"] = 0,
["weights"] = {
[1] = 1,
[2] = 1
}
},
[2] = {
["bias"] = 1,
["netInput"] = 0,
["value"] = 0,
["weights"] = {
[1] = 1,
[2] = 1
}
},
[3] = {
["bias"] = 1,
["netInput"] = 0,
["value"] = 0,
["weights"] = {
[1] = 1,
[2] = 1
}
},
[4] = {
["bias"] = 1,
["netInput"] = 0,
["value"] = 0,
["weights"] = {
[1] = 1,
[2] = 1
}
}
},
[3] = {
[1] = {
["bias"] = 1,
["netInput"] = 0,
["value"] = 0,
["weights"] = {
[1] = 1,
[2] = 1,
[3] = 1,
[4] = 1
}
}
}
}
})
attempts = 1000
for i = 1,attempts do
network:backPropagate({0,0},{0})
network:backPropagate({1,0},{1})
network:backPropagate({0,1},{1})
network:backPropagate({1,1},{0})
end
print("Results:")
print("input (0 0) | "..network:feedForward({0,0})[1])
print("input (1 0) | "..network:feedForward({1,0})[1])
print("input (0 1) | "..network:feedForward({0,1})[1])
print("input (1 1) | "..network:feedForward({1,1})[1])
- Library -
local nn = {}
nn.__index = nn
nn.ActivationFunctions = {
sigmoid = function(x) return 1/(1+math.exp(-x/1)) end,
ReLu = function(x) return math.max(0, x) end,
}
nn.Derivatives = {
sigmoid = function(x) return x * (1 - x) end,
ReLu = function(x) return x > 0 and 1 or 0 end,
}
nn.CostFunctions = {
MSE = function(outputs, expected)
local sum = 0
for i = 1, #outputs do
sum += 1/2*(expected[i] - outputs[i])^2
end
return sum/#outputs
end,
}
function nn.new(inputs, outputs, hiddenLayers, neurons, learningRate, activationFunction)
local self = setmetatable({}, nn)
self.learningRate = learningRate or .3
self.activationFunction = activationFunction or "ReLu"
self.net = {}
local net = self.net
local layers = hiddenLayers+2
for i = 1, layers do
net[i] = {}
end
for i = 1, inputs do
net[1][i] = {value = 0}
end
for i = 2, layers-1 do
for x = 1, neurons do
net[i][x] = {netInput = 0, value = 0, bias = math.random()*2-1, weights = {}}
for z = 1, #net[i-1] do
net[i][x].weights[z] = math.random()*2-1
end
end
end
for i = 1, outputs do
net[layers][i] = {netInput = 0, value = 0, bias = math.random()*2-1, weights = {}}
for z = 1, #net[layers-1] do
net[layers][i].weights[z] = math.random()*2-1
end
end
return self
end
function nn.newFromRawData(data)
return setmetatable(data, nn)
end
function nn:feedForward(inputs)
local net = self.net
local activation = self.activationFunction
local layers = #net
local inputLayer = net[1]
local outputLayer = net[layers]
for i = 1, #inputLayer do
inputLayer[i].value = inputs[i]
end
for i = 2, layers do
local layer = net[i]
for x = 1, #layer do
local sum = layer[x].bias
for z = 1, #net[i-1] do
sum += net[i-1][z].value * layer[x].weights[z]
end
layer[x].netInput = sum
layer[x].value = nn.ActivationFunctions[activation](sum)
end
end
local outputs = {}
for i = 1, #outputLayer do
table.insert(outputs, outputLayer[i].value)
end
return outputs
end
function nn:backPropagate(inputs, expected)
local outputs = self:feedForward(inputs)
local net = self.net
local activation = self.activationFunction
local layers = #net
local lr = self.learningRate
local inputLayer = net[1]
local outputLayer = net[layers]
for i = 1, #outputLayer do
local delta = -(expected[i] - outputs[i]) * nn.Derivatives[activation](net[layers][i].value)
outputLayer[i].delta = delta
end
for i = layers-1, 2, -1 do
local layer = net[i]
local nextLayer = net[i+1]
for x = 1, #layer do
local delta = 0
for z = 1, #nextLayer do
delta += nextLayer[z].delta * nextLayer[z].weights[x]
end
layer[x].delta = delta * nn.Derivatives[activation](layer[x].value)
end
end
for i = 2, layers do
local lastLayer = net[i-1]
for x = 1, #net[i] do
net[i][x].bias -= lr * net[i][x].delta
for z = 1, #lastLayer do
net[i][x].weights[z] -= lr * net[i][x].delta * lastLayer[z].value
end
end
end
end
return nn
Any help would be highly appreciated, thanks!
All initial weights must be DIFFERENT numbers, otherwise backpropagation will not work. For example, you can replace 1 with math.random()
Increase number of attempts to 10000
With these modifications, your code works fine:
Results:
input (0 0) | 0.028138230938126
input (1 0) | 0.97809448578087
input (0 1) | 0.97785000216126
input (1 1) | 0.023128477689456
I am trying to make a table and want to change a value in that table for a particular key. The thing is when I do change the key it does change for all the keys.
function dump(o, nb)
if nb == nil then
nb = 0
end
if type(o) == 'table' then
local s = ''
for i = 1, nb + 1, 1 do
s = s .. " "
end
s = '{\n'
for k,v in pairs(o) do
if type(k) ~= 'number' then k = '"'..k..'"' end
for i = 1, nb, 1 do
s = s .. " "
end
s = s .. '['..k..'] = ' .. dump(v, nb + 1) .. ',\n'
end
for i = 1, nb, 1 do
s = s .. " "
end
return s .. '}'
else
return tostring(o)
end
end
Config={}
PlayersStatusTable={}
Config.DefaultStatus = {
hunger = 1000000,
thirst = 1000000,
}
local timeNow = os.clock()
PlayersStatusTable[12] = Config.DefaultStatus
PlayersStatusTable[112] = Config.DefaultStatus
PlayersStatusTable[54] = Config.DefaultStatus
for playerId, details in pairs(PlayersStatusTable) do
print("playerid1",playerId)
print(dump(PlayersStatusTable))
print(dump(PlayersStatusTable[112]))
print(dump(PlayersStatusTable[112].hunger))
PlayersStatusTable[112].hunger = 5
end
the output is this:
playerid1 112
{
[112] = {
["thirst"] = 1000000,
["hunger"] = 1000000,
},
[54] = {
["thirst"] = 1000000,
["hunger"] = 1000000,
},
[12] = {
["thirst"] = 1000000,
["hunger"] = 1000000,
},
}
{
["thirst"] = 1000000,
["hunger"] = 1000000,
}
1000000
playerid1 54
{
[112] = {
["thirst"] = 1000000,
["hunger"] = 5,
},
[54] = {
["thirst"] = 1000000,
["hunger"] = 5,
},
[12] = {
["thirst"] = 1000000,
["hunger"] = 5,
},
}
{
["thirst"] = 1000000,
["hunger"] = 5,
}
5
playerid1 12
{
[112] = {
["thirst"] = 1000000,
["hunger"] = 5,
},
[54] = {
["thirst"] = 1000000,
["hunger"] = 5,
},
[12] = {
["thirst"] = 1000000,
["hunger"] = 5,
},
}
{
["thirst"] = 1000000,
["hunger"] = 5,
}
5
I just want the hunger of id 112 to be 5.
You're assigning the same table to all 3 keys, so they all point to the same table that's being changed. You need to ensure that you're creating a new table when you assign to each key.
local function shallowCopy(t)
local result = {}
for k, v in pairs(t) do
result[k] = v
end
return result
end
PlayersStatusTable[12] = shallowCopy(Config.DefaultStatus)
I am inserting into a table like this
Admin = {}
table.insert(Admins, {id = playerId, Count = 0})
And that works fine.
How do I remove that specific admin from that table now?
The following does not work, and Im sure its because ID is stored in an array that's inside of the table, but how would I access that then?
table.remove(Admins, playerId)
Basically,
I want to remove from the table Admins, where the ID == playerId that I input.
There are two approaches to remove an entry from the table, both are acceptable ways:
1. myTable[index] = nil Removes an entry from given index, but adds a hole in the table by maintaining the indices
local Admins = {}
table.insert(Admins, {id = 10, Count = 0})
table.insert(Admins, {id = 20, Count = 1})
table.insert(Admins, {id = 30, Count = 2})
table.insert(Admins, {id = 40, Count = 3})
local function removebyKey(tab, val)
for i, v in ipairs (tab) do
if (v.id == val) then
tab[i] = nil
end
end
end
-- Before
-- [1] = {['Count'] = 0, ['id'] = 10},
-- [2] = {['Count'] = 1, ['id'] = 20},
-- [3] = {['Count'] = 2, ['id'] = 30},
-- [4] = {['Count'] = 3, ['id'] = 40}}
removebyKey(Admins, 20)
-- After
-- [1] = {['Count'] = 0, ['id'] = 10},
-- [3] = {['Count'] = 2, ['id'] = 30},
-- [4] = {['Count'] = 3, ['id'] = 40}
2. table.remove(myTable, index)
Removes the entry from given index and renumbering the indices
local function getIndex(tab, val)
local index = nil
for i, v in ipairs (tab) do
if (v.id == val) then
index = i
end
end
return index
end
local idx = getIndex(Admins, 20) -- id = 20 found at idx = 2
if idx == nil then
print("Key does not exist")
else
table.remove(Admins, idx) -- remove Table[2] and shift remaining entries
end
-- Before is same as above
-- After entry is removed. Table indices are changed
-- [1] = {['id'] = 10, ['Count'] = 0},
-- [2] = {['id'] = 30, ['Count'] = 2},
-- [3] = {['id'] = 40, ['Count'] = 3}
What I have is a nested table (two-levels). What I'm looking to do is merge (flatten?) the sub-tables, with any duplicates spilling over into new keys.
So the input would look like
t = {
[1] = {
[1] = "One",
[3] = "Three"
},
[2] = {
[2] = "Two",
[3] = "Three"
},
[3] = {
[1] = "One",
[2] = "Two",
[4] = "Four"
}
}
and the output would look like
t = {
[1] = {
[1] = "One",
[2] = "Two",
[3] = "Three",
[4] = "Four"
}
[2] = {
[1] = "One",
[2] = "Two",
[3] = "Three"
}
}
The input table would be up to 1000 keys, so I'm hoping it can be done efficiently.
local bottom, max_btm = {}, 0
for top = #t, 1, -1 do
for k, v in pairs(t[top]) do
local btm = bottom[k] or 0
if btm < top then
repeat btm = btm + 1
until btm == top or not t[btm][k]
if btm ~= top then
t[btm][k], t[top][k] = v
end
bottom[k] = btm
max_btm = math.max(max_btm, btm)
end
end
if max_btm < top then
t[top] = nil
end
end
Try this:
local d={}
for k,v in pairs(t) do
if k~=1 then
for kk,vv in pairs(v) do
if t[1][kk]==nil then
t[1][kk]=vv
else
d[kk]=vv
end
end
t[k]=nil
end
end
t[2]=d
Thank you for all the support with lua, I'm very new and my application is working nicely so far.
I've made an app that will take in a few thousand numbers for different items. I'm trying to find the Mean/Media/Mode for each item, I've been able to do this for a single item, but not for all items.
Here is my table structure:
for i = 0, 1500 do
local e = {}
e.seller,
e.buyer,
e.itemName,
e.soldAmount = GetSoldAmount(FromMember(i))
table.insert(allSalesTempTable, e)
end
Table output format
[1] =
{
["itemName"] = [[Salad]]
["buyer"] = [[#Mike]],
["eventType"] = 15,
["soldAmount"] = 150,
["seller"] = [[#Sarah]],
},
[2] =
{
["itemName"] = [Pizza]
["buyer"] = [[#James]],
["eventType"] = 15,
["soldAmount"] = 150,
["seller"] = [[#Sarah]],
},
[3] =
{
["itemName"] = [Salad]
["buyer"] = [[#Frank]],
["eventType"] = 15,
["soldAmount"] = 75,
["seller"] = [[#Sarah]],
},
[4] ...
},
Then I'm trying to send the table/array to this mean function
stats={}
-- Get the mean value of a table
function stats.mean( t )
local sum = 0
local count= 0
local tempTbl = {}
(This is completely not going to work, but its what I'm trying so far)
for k,v in pairs(t) do tempTbl[k] = v
if v.itemName == tempTbl.itemName then
sum = sum + v.soldAmount
count = count + 1
end
end
return (sum / count)
end
--- To get the function started
stats.mean(e)
Here is where I'm getting fuzzy, Not sure if I can add the MEAN while collecting the data into the first temp table, or if it needs to be re-calculated after I have all the data?
If it needs to be done after, then my stats.mean(e) needs a way to insert it?
I'm trying to get this output:
[1] =
{
["itemName"] = [[Salad]]
["buyer"] = [[#Mike]],
["eventType"] = 15,
["soldAmount"] = 150,
["seller"] = [[#Sarah]],
["mean"] = 112.5 - New Insert somehow
},
[2] =
{
["itemName"] = [Pizza]
["buyer"] = [[#James]],
["eventType"] = 15,
["soldAmount"] = 150,
["seller"] = [[#Sarah]],
["mean"] = 150 - New Insert somehow
},
[3] =
{
["itemName"] = [Salad]
["buyer"] = [[#Frank]],
["eventType"] = 15,
["soldAmount"] = 75,
["seller"] = [[#Sarah]],
["mean"] = 112.5 - New Insert somehow
},
[4] ...
},
I've been working on this problem for a few days, After I see how to adjust my format for the mean and insert into the existing table I'll be able to figure out mean/min/max/median/mode easy enough.