i want to create multiple lists so that the user can input variable values into each successive list
my current idea is to create using for loop
if T.startswith('y'):
num_o = int(float(input("number of wanted orbiting objects : ")))
for i in range (num_o):
i am hoping to achieve a result with equivalent result to this
where the resulting lists can be filled
Ms = int(float(input("mass of central object is : "))) # for masses
Me = int(float(input("mass of orbit 1 object is : ")))
Mm = int(float(input("mass of orbit 2 object is : ")))
Mc = int(float(input("mass of orbit 3 object is : ")))
...
Related
Assume I have the following matrix:
X = np.array([[1,2,3], [4,5,6], [7,8,9], [70,80,90], [45,43,68], [112,87,245]])
I want to draw a batch of 2 random rows at each time loop, and send it to a function. For instance, a batch in iteration i can be batch = [[4,5,6], [70,80,90]]
I do the following:
X = np.array([[1,2,3], [4,5,6], [7,8,9], [70,80,90], [45,43,68], [112,87,245]])
def caclulate_batch(batch):
pass
for i in range(X.shape[0]/2):
batch = np.array([])
for _ in range(2):
r = random.randint(0, 5)
batch = np.append(batch, X[r])
caclulate_batch(batch)
There are two problems here: (1) It returns appended array (2) The random number can be repeated which can choose the same row many times. How can modify the code to fit my requirement.
r = np.random.randint(0, len(x), 2) should get you the indices. That lets you use fancy indexing to get the subset: batch = x[r, :].
If you want to accumulate arrays along a new dimension, as your loop does, use np.stack or np.block instead of np.append.
(1) You can use numpy.stack instead of append. EDIT: But this function would be called when you have all your batch in a list like:
list = ([1,2], [3,4])
numpy.stack(list)
# gives [[1,2],
# [3,4]]
(2) You can shuffle X array, loop through the results and extract two by two. Look at numpy.random.shuffle
It would look like that:
S = np.random.shuffle(X)
for i in range(S.shape[0]/2):
batch = S[i*2:i*2+1]
caclulate_batch(batch)
I want to use compute_gradients and generate local gradients. These gradients are to be averaged with multiple local gradients from other machines after which apply_gradients will be called. I am using 2 session.runs with a feed_dict in the second one that accepts gradients. Since apply_gradients expects a list of tuples, I am looking for an efficient way to do this.
This is how I am generating the list of tuples placeholder :
grads = cifar10.train_part1(loss, global_step)
xx = [tf.placeholder(tf.float32, shape=grads[0][0].shape) for i in range(10)]
yy = [tf.placeholder(tf.float32, shape=grads[0][0].shape) for i in range(10)]
xyz = zip(xx,yy)
train_op = cifar10.train_part2(loss,global_step, xyz)
I get the following error :
NotImplementedError: ('Trying to optimize unsupported type ', tf.Tensor 'Placeholder_10:0' shape=(5, 5, 3, 64) dtype=float32)
I have three lists that look like this:
age = ['51+', '21-30', '41-50', '31-40', '<21']
cluster = ['notarget', 'cluster3', 'allclusters', 'cluster1', 'cluster2']
device = ['htc_one_2gb','iphone_6/6+_at&t','iphone_6/6+_vzn','iphone_6/6+_all_other_devices','htc_one_2gb_limited_time_offer','nokia_lumia_v3','iphone5s','htc_one_1gb','nokia_lumia_v3_more_everything']
I also have column in a df that looks like this:
campaign_name
0 notarget_<21_nokia_lumia_v3
1 htc_one_1gb_21-30_notarget
2 41-50_htc_one_2gb_cluster3
3 <21_htc_one_2gb_limited_time_offer_notarget
4 51+_cluster3_iphone_6/6+_all_other_devices
I want to split the column into three separate columns based on the values in the above lists. Like so:
age cluster device
0 <21 notarget nokia_lumia_v3
1 21-30 notarget htc_one_1gb
2 41-50 cluster3 htc_one_2gb
3 <21 notarget htc_one_2gb_limited_time_offer
4 51+ cluster3 iphone_6/6+_all_other_devices
First thought was to do a simple test like this:
ages_list = []
for i in ages:
if i in df['campaign_name'][0]:
ages_list.append(i)
print ages_list
>>> ['<21']
I was then going to convert ages_list to a series and combine it with the remaining two to get the end result above but i assume there is a more pythonic way of doing it?
the idea behind this is that you'll create a regular expression based on the values you already have , for example if you want to build a regular expressions that capture any value from your age list you may do something like this '|'.join(age) and so on for all the values you already have cluster & device.
a special case for device list becuase it contains + sign that will conflict with the regex ( because + means one or more when it comes to regex ) so we can fix this issue by replacing any value of + with \+ , so this mean I want to capture literally +
df = pd.DataFrame({'campaign_name' : ['notarget_<21_nokia_lumia_v3' , 'htc_one_1gb_21-30_notarget' , '41-50_htc_one_2gb_cluster3' , '<21_htc_one_2gb_limited_time_offer_notarget' , '51+_cluster3_iphone_6/6+_all_other_devices'] })
def split_df(df):
campaign_name = df['campaign_name']
df['age'] = re.findall('|'.join(age) , campaign_name)[0]
df['cluster'] = re.findall('|'.join(cluster) , campaign_name)[0]
df['device'] = re.findall('|'.join([x.replace('+' , '\+') for x in device ]) , campaign_name)[0]
return df
df.apply(split_df, axis = 1 )
if you want to drop the original column you can do this
df.apply(split_df, axis = 1 ).drop( 'campaign_name', axis = 1)
Here I'm assuming that a value must be matched by regex but if this is not the case you can do your checks , you got the idea
I am wondering if there is a way to transform a matrix of 2 columns into a multimap or list of list.
The first column of the matrix is an id (with possibly duplicated entries) and the 2nd column is some value.
For example,
if I have to following matrix
m <- matrix(c(1,2,1,3,2,4), c(3,2))
I would like to transform it into the following list
[[1]]
3,4
[[2]]
2
With base functions, you can do something like this:
tapply(m[,2], m[,1], `[`) # outputs an array
by(m, m[,1], function(m) m[,2]) # outputs a by object, which is a list
You could use plyr:
dlply(m, 1, function(m) m[,2]) # outputs a list
dlply(m, 1, `[`, 2) # another way to do it...
I am seeing a problem with some Scala 2.7.7 code I'm working on, that should not happen if it the equivalent was written in Java. Loosely, the code goes creates a bunch of card players and assigns them to tables.
class Player(val playerNumber : Int)
class Table (val tableNumber : Int) {
var players : List[Player] = List()
def registerPlayer(player : Player) {
println("Registering player " + player.playerNumber + " on table " + tableNumber)
players = player :: players
}
}
object PlayerRegistrar {
def assignPlayersToTables(playSamplesToExecute : Int, playersPerTable:Int) = {
val numTables = playSamplesToExecute / playersPerTable
val tables = (1 to numTables).map(new Table(_))
assert(tables.size == numTables)
(0 until playSamplesToExecute).foreach {playSample =>
val tableNumber : Int = playSample % numTables
tables(tableNumber).registerPlayer(new Player(playSample))
}
tables
}
}
The PlayerRegistrar assigns a number of players between tables. First, it works out how many tables it will need to break up the players between and creates a List of them.
Then in the second part of the code, it works out which table a player should be assigned to, pulls that table from the list and registers a new player on that table.
The list of players on a table is a var, and is overwritten each time registerPlayer() is called. I have checked that this works correctly through a simple TestNG test:
#Test def testRegisterPlayer_multiplePlayers() {
val table = new Table(1)
(1 to 10).foreach { playerNumber =>
val player = new Player(playerNumber)
table.registerPlayer(player)
assert(table.players.contains(player))
assert(table.players.length == playerNumber)
}
}
I then test the table assignment:
#Test def testAssignPlayerToTables_1table() = {
val tables = PlayerRegistrar.assignPlayersToTables(10, 10)
assertEquals(tables.length, 1)
assertEquals(tables(0).players.length, 10)
}
The test fails with "expected:<10> but was:<0>". I've been scratching my head, but can't work out why registerPlayer() isn't mutating the table in the list. Any help would be appreciated.
The reason is that in the assignPlayersToTables method, you are creating a new Table object. You can confirm this by adding some debugging into the loop:
val tableNumber : Int = playSample % numTables
println(tables(tableNumber))
tables(tableNumber).registerPlayer(new Player(playSample))
Yielding something like:
Main$$anon$1$Table#5c73a7ab
Registering player 0 on table 1
Main$$anon$1$Table#21f8c6df
Registering player 1 on table 1
Main$$anon$1$Table#53c86be5
Registering player 2 on table 1
Note how the memory address of the table is different for each call.
The reason for this behaviour is that a Range is non-strict in Scala (until Scala 2.8, anyway). This means that the call to the range is not evaluated until it's needed. So you think you're getting back a list of Table objects, but actually you're getting back a range which is evaluated (instantiating a new Table object) each time you call it. Again, you can confirm this by adding some debugging:
val tables = (1 to numTables).map(new Table(_))
println(tables)
Which gives you:
RangeM(Main$$anon$1$Table#5492bbba)
To do what you want, add a toList to the end:
val tables = (1 to numTables).map(new Table(_)).toList
val tables = (1 to numTables).map(new Table(_))
This line seems to be causing all the trouble - mapping over 1 to n gives you a RandomAccessSeq.Projection, and to be honest, I don't know how exactly they work, but a bit less clever initialising technique does the job.
var tables: Array[Table] = new Array(numTables)
for (i <- 0 to numTables) tables(i) = new Table(i)
Using the first initialisation method I wasn't able to change the objects (just like you), but using a simple array everything seems to be working.